Oil Spill Science and Technology

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Oil Spill Science and Technology

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Oil Spill Science and Technology Prevention, Response, and Cleanup

Edited by

Mervin Fingas

Amsterdam l Boston l Heidelberg l London l New York l Oxford Paris l San Diego l San Francisco l Singapore l Sydney l Tokyo Gulf Professional Publishing is an imprint of Elsevier

Gulf Professional Publishing is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, UK Copyright Ó 2011 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging in Publication Data Oil spill science and technology : prevention, response, and clean up / edited by Mervin Fingas. – 1st ed. p. cm. Summary: “The National Academy of Sciences estimate that 1.7 to 8.8 million tons of oil are released into world’s water every year, of which more than 70% is directly related to human activities. The effects of these spills are all too apparent: dead wildlife, oil covered marshlands and contaminated water chief among them. This reference will provide scientists, engineers and practitioners with the latest methods use for identify and eliminating spills before they occur and develop the best available techniques, equipment and materials for dealing with oil spills in every environment. Topics covered include: spill dynamics and behaviour, spill treating agents, and cleanup techniques such as: in situ burning, mechanical containment or recovery, chemical and biological methods and physical methods are used to clean up shorelines. Also included are the fate and effects of oil spills and means to assess damage”– Provided by publisher. ISBN 978-1-85617-943-0 1. Oil spills–Prevention. 2. Oil spills–Cleanup. 3. Oil spils–Managements. I. Fingas, Mervin F. TD427.P4O38785 2010 628.1’6833–dc22 2010033465 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. ISBN: 978-1-85617-943-0 For information on all Gulf Professional Publishing publications visit our Web site at www.elsevierdirect.com

11 12 13 10 9 8 7 6 5 4 3 2 1 Printed and bound in the USA

Contents

Preface About the Contributors

xxv xxvii

Part I Introduction and the Oil Spill Problem 1.

2.

Introduction

3

Merv Fingas 1.1. Introduction 1.2. A Word on the Frequency of Spills

3 4

Spill Occurrences: A World Overview

7

Dagmar Schmidt-Etkin 2.1. Introduction 2.2. Executive Summary 2.3. Overview of Spill Occurrences 2.3.1. Natural Oil Seepage 2.3.2. Historical Concern Over Oil Pollution 2.3.3. Sources of Oil Spills and Patterns of Spillage 2.3.4. Spillage from Oil Exploration and Production Activities 2.3.5. Spills During Oil Transport 2.3.6. Spillage from Oil Refining 2.3.7. Spillage Related to Oil Consumption and Usage 2.3.8. Oil Inputs from Potentially Polluting Sunken Shipwrecks 2.3.9. Summary of Oil Spillage References

7 8 8 8 11 12 17 23 28 32 39 41 46

Part II Types of Oils and Their Properties 3.

Introduction to Oil Chemistry and Properties

51

Merv Fingas 3.1. Introduction

51

v

vi

Contents

3.2. The Composition of Oil 3.3. Properties of Oil References

51 54 59

Part III Oil Analysis and Remote Sensing 4.

5.

Measurement of Oil Physical Properties

63

Bruce Hollebone 4.1. Introduction 4.2. Bulk Properties of Crude Oil and Fuel Products 4.2.1. Density and API Gravity 4.2.2. Dynamic Viscosity 4.2.3. Surface and Interfacial Tensions 4.2.4. Flash Point 4.2.5. Pour Point 4.2.6. Sulphur Content 4.2.7. Water Content 4.2.8. Evaluation of the Stability of Emulsions Formed from Brine and Oils and Oil Products 4.2.9. Evaluation of the Relative Dispersability of Oil and Oil Products 4.2.10. Adhesion to Stainless Steel 4.3. Hydrocarbon Groups 4.4. Quality Assurance and Control 4.5. Effects of Evaporative Weathering on Oil Bulk Properties 4.5.1. Weathering 4.5.2. Preparing Evaporated (Weathered) Samples of Oils 4.5.3. Quantifying Equation(s) for Predicting Evaporation References Appendix 4.1

71 72 73 77 78 78 79 81 83 85

Introduction to Oil Chemical Analysis

87

Merv Fingas 5.1. Introduction 5.2. Sampling and Laboratory Analysis 5.2.1. Incorrect and Obsolete Methods 5.3. Chromatography 5.3.1. Introduction to Gas Chromatography 5.3.2. Methodology 5.4. Identification and Forensic Analysis 5.4.1. Biomarkers 5.4.2. Sesquiterpanes and Diamondoids 5.5. Field Analysis References

63 63 66 67 67 69 70 70 70 71

87 87 88 89 89 93 96 99 105 107 107

Contents

6.

Oil Spill Remote Sensing: A Review Merv Fingas and Carl E. Brown 6.1. Introduction 6.2. Visible Indications of Oil 6.3. Optical Sensors 6.3.1. Visible 6.3.2. Infrared 6.3.3. Ultraviolet 6.4. Laser Fluorosensors 6.5. Microwave Sensors 6.5.1. Radiometers 6.5.2. Radar 6.5.3. Microwave Scatterometers 6.5.4. Surface Wave Radars 6.5.5. Interferometric Radar 6.6. Slick Thickness Determination 6.6.1. Visual Thickness Indications 6.6.2. Slick Thickness Relationships in Remote Sensors 6.6.3. Specific Thickness Sensors 6.7. Acoustic Systems 6.8. Integrated Airborne Sensor Systems 6.9. Satellite Remote Sensing 6.10. Oil Under Ice Detection 6.11. Underwater Detection and Tracking 6.12. Small Remote-Controlled Aircraft 6.13. Real-Time Displays and Printers 6.14. Routine Surveillance 6.15. Future Trends 6.16. Recommendations Acknowledgments References

7.

vii

111 111 112 114 114 120 123 123 124 124 125 134 135 135 135 135 136 138 139 139 140 144 145 149 150 150 153 154 158 158

Laser Fluorosensors

171

Carl E. Brown 7.1. Principles of Operation 7.1.1. Active versus Passive Sensors 7.1.2. Sensor Features 7.1.3. Pros/Cons 7.2. Oil Classification 7.2.1. Real-Time Analysis 7.2.2. Sensor Outputs 7.3. Existing Operational Units 7.3.1. Airborne 7.3.2. Ship-Borne 7.4. Aircraft Requirements 7.4.1. Power

171 171 171 174 175 175 176 179 179 179 180 180

viii

Contents

7.4.2. Weight 7.4.3. Operational Altitude 7.5. Cost Estimates 7.6. Conclusions References

181 181 182 182 182

Part IV Behaviour of Oil in the Environment and Spill Modeling 8.

9.

Introduction to Spill Modeling

187

Merv Fingas 8.1. Introduction 8.2. An Overview of Weathering 8.2.1. Evaporation 8.2.2. Emulsification 8.2.3. Natural Dispersion 8.2.4. Dissolution 8.2.5. Photo-Oxidation 8.2.6. Sedimentation, Adhesion to Surfaces, and Oil-Fines Interaction 8.2.7. Biodegradation 8.2.8. Sinking and Overwashing 8.2.9. Formation of Tarballs 8.3. Movement of Oil and Oil Spill Modeling 8.3.1. Spreading 8.3.2. Movement of Oil Slicks 8.3.3. Spill Modeling References

192 193 194 195 196 196 197 198 199

Evaporation Modeling

201

Merv Fingas 9.1. Introduction 9.2. Review of Theoretical Concepts 9.3. Development of New Diffusion-Regulated Models 9.3.1. Wind Experiments 9.3.2. Evaporation Rate and Area 9.3.3. Study of Mass and Evaporation Rate 9.3.4. Study of the Evaporation of Pure Hydrocarbonsdwith and Without Wind 9.3.5. Other Factors 9.3.6. Temperature Variation and Generic Equations Using Distillation Data 9.3.7. A Simplified Means of Estimation 9.4. Complexities to the Diffusion-Regulated Model 9.4.1. Thickness of the Oil

187 187 188 190 191 192 192

201 205 212 212 215 215 216 217 217 227 229 229

Contents

9.4.2. The Bottle Effect 9.4.3. Skinning 9.4.4. Rises from the 0-Wind Values 9.5. Use of Evaporation Equations in Spill Models 9.6. Comparison of Model Approaches 9.7. Summary References

10.

11.

ix 229 230 233 233 235 240 241

Models for Water-in-Oil Emulsion Formation

243

Merv Fingas 10.1. Introduction 10.2. Early Modeling of Emulsification 10.3. First Two Model Developments 10.4. New Model Development 10.5. Development of an Emulsion Kinetics Estimator 10.6. Discussion 10.7. Conclusions References

243 249 251 253 260 260 269 270

Oil Spill Trajectory Forecasting Uncertainty and Emergency Response

275

Debra 11.1. 11.2. 11.3.

Simecek-Beatty Introduction: The Importance of Forecast Uncertainty The Basics of Oil Spill Modeling Trajectory Model Uncertainties 11.3.1. Release Details 11.3.2. Wind 11.3.3. Current 11.3.4. Turbulent Diffusion 11.3.5. Oil Weathering 11.3.6. Ensemble Forecasting 11.3.7. Communicating Trajectory Forecast Uncertainty 11.4. Trajectory Forecast Verification 11.4.1. Diagnostic Verification 11.5. Summary and Conclusions Acknowledgments References

275 276 280 281 282 284 287 288 289 291 292 294 295 297 297

Part V Physical Spill Countermeasures on Water 12.

Physical Spill Countermeasures

303

Merv Fingas 12.1. Containment on Water

303

x

13.

Contents

12.1.1. Types of Booms and Their Construction 12.1.2. Uses of Booms 12.1.3. Boom Failures 12.1.4. Ancillary Equipment 12.1.5. Sorbent Booms and Barriers 12.1.6. Special-Purpose Booms 12.2. Skimmers 12.2.1. Oleophilic Surface Skimmers 12.2.2. Weir Skimmers 12.2.3. Suction or Vacuum Skimmers 12.2.4. Elevating Skimmers 12.2.5. Submersion Skimmers 12.2.6. Skimmer Performance 12.2.7. Special-Purpose Ships 12.3. Sorbents 12.4. Manual Recovery 12.5. Temporary Storage 12.6. Pumps 12.6.1. Performance of Pumps 12.7. Separation 12.8. Disposal Acknowledgments References

303 306 309 313 314 314 315 316 320 321 322 323 323 325 325 329 330 332 334 334 335 337 337

Weather Effects on Oil Spill Countermeasures

339

Merv Fingas 13.1. Introduction 13.1.1. Spreading Compared to Weathering 13.1.2. Important Components of Weather 13.1.3. Oil Properties Regardless of Weathering 13.2. Review of Literature on Spill Countermeasures and Weather 13.2.1. A Priori Decision Guides 13.2.2. General Countermeasures 13.2.3. Booms 13.2.4. Skimmers 13.2.5. Dispersants 13.2.6. In-Situ Burning 13.2.7. Others 13.2.8. Ice Conditions 13.3. Development of Models for Effectiveness of Countermeasures 13.3.1. Overall 13.3.2. Booms 13.3.3. Skimmers 13.3.4. Dispersants 13.3.5. In-Situ Burning

339 340 340 343 343 343 345 345 353 372 378 381 381 383 383 383 383 398 403

Contents

13.3.6. Others 13.4. Overview of Weather Limitations 13.5. Summary and Conclusions Acknowledgments References

xi 404 405 407 416 416

Part VI Treating Agents 14.

15.

Spill-Treating Agents

429

Merv Fingas 14.1. Introduction 14.2. Dispersants 14.3. Surface-Washing Agents 14.4. Emulsion Breakers and Inhibitors 14.5. Recovery Enhancers 14.6. Solidifiers 14.7. Sinking Agents 14.8. Biodegradation Agents

429 429 430 430 431 431 431 432

Oil Spill Dispersants: A Technical Summary

435

Merv Fingas 15.1. Introduction 15.1.1. What Are Dispersants? 15.2. The Basic Physics and Chemistry of Dispersants 15.2.1. Formulations 15.2.2. Nature of Surfactant Interaction with Oil 15.3. The Basic Nature of Dispersions or Oil-in-Water Emulsions 15.3.1. Forces of Destabilization 15.3.2. The Science of Stabilization 15.3.3. Oil Spill Dispersions 15.3.4. Significance of Emulsion Stability 15.4. Effectiveness 15.4.1. Introduction to Effectiveness 15.4.2. Field Trials 15.4.3. Laboratory Tests 15.4.4. Tank Tests 15.4.5. Analytical Means 15.5. Monitoring 15.5.1. Introduction to Monitoring 15.5.2. Review of SMART Protocol 15.5.3. The SERVS Protocol 15.5.4. Review of Other Protocols 15.5.5. Review of Goodman Analysis of SMART 15.5.6. Considerations for Monitoring in the Field

435 437 437 437 438 440 441 443 447 449 451 452 454 464 467 480 481 481 482 483 486 487 488

xii

Contents

15.5.7. 15.5.8. 15.5.9. 15.5.10. 15.5.11. 15.5.12. 15.5.13. 15.5.14. 15.5.15. 15.6.

15.7.

15.8. 15.9.

15.10.

Visual Surveillance Remote Sensing Tracking of Oil on Surface Tracking of Oil Underwater Mass Balance Use of Undispersed Slick(s) as a Control Background Levels of Hydrocarbons Using and Computing Values Recommended Procedures for Monitoring Dispersant Applications Studies Energy Composition of Oil Amount of Dispersant Temperature Salinity Particle or Droplet Size

Physical 15.6.1. 15.6.2. 15.6.3. 15.6.4. 15.6.5. 15.6.6. Toxicity 15.7.1. Toxicity of Dispersants 15.7.2. Photoenhanced Toxicity 15.7.3. Testing Protocols Biodegradation Other Information 15.9.1. Component Separation 15.9.2. Dispersant Use 15.9.3. Application of Dispersants 15.9.4. Assessment of the Use of Dispersants 15.9.5. Spills-of-Opportunity Research 15.9.6. Interaction with Sediment Particles 15.9.7. Modeling Oil and Dispersed Oil Behavior and Fate 15.9.8. Separation of Dispersants from Water 15.9.9. Dispersant Breakthrough Oil Slicks 15.9.10. Overall Effects of Weather on Dispersion 15.9.11. Joint Effect of Temperature and Salinity on Effectiveness 15.9.12. Dispersibility of Biodiesels 15.9.13. Application Systems 15.9.14. Accelerated Weathering Summary and Conclusions 15.10.1. Effectiveness Testing Overall 15.10.2. Laboratory Effectiveness Tests 15.10.3. Tank Testing 15.10.4. Analytical Methods for Effectiveness 15.10.5. Toxicity of Dispersed Oil and Dispersants 15.10.6. Biodegradation of Oil Treated by Dispersants

492 493 494 494 494 495 495 496 496 500 500 506 512 512 513 519 519 532 533 534 535 539 539 539 551 553 555 555 556 557 557 557 558 559 560 560 562 563 563 564 564 564 565

xiii

Contents

15.10.7. 15.10.8. 15.10.9. 15.10.10. 15.10.11. 15.10.12. 15.10.13. 15.10.14. Acknowledgments References

16.

Spill-of-Opportunity Research 565 Monitoring Dispersant Applications 565 Dispersant Use in Recent Times 566 Interaction with Sediment Particles 566 Stability of Dispersions and Resurfacing with Time 566 Fate of Dispersed Oil 566 Application Technology and Issues 566 Correlation of Oil Properties with Effectiveness 566 566 567

A Practical Guide to Chemical Dispersion for Oil Spills Merv Fingas 16.1. Introduction and Decision Making 16.1.1. An OverviewdHow, When, and Where Dispersants Are Used 16.1.2. Net Environmental Benefit Analysis 16.1.3. Scenarios For Which Dispersants Might Be Used 16.1.4. Planning Process and Checklists 16.2. How Dispersants Are Used 16.2.1. Dispersion Spray Equipment 16.2.2. Spray Aircraft 16.2.3. Spray Nomograms and Calculations 16.2.4. Monitoring, Sampling, and Analytical Equipment 16.2.5. Equipment Availability 16.2.6. Equipment Checklist 16.2.7. Conducting the Operation 16.3. Safety and Postdispersion Actions 16.3.1. Worker Health and Safety Precautions 16.3.2. Follow-Up Monitoring Additional Information Appendix A. Specific Spill Scenarios and Dispersion Strategies Appendix B. Nomograms to Calculate Spreading and Viscosity with Time

17.

583 583 584 587 589 589 591 592 593 594 596 596 597 597 598 598 599 601 603 605

Procedures for the Testing and Approval of Oil Spill Treatment Products in the United Kingdomd What They Are and Considerations for Development

611

Mark Kirby 17.1. Background and Introduction 17.1.1. Preassessment Requirements 17.2. Toxicity Testing Procedures

611 612 613

xiv

18.

Contents

17.2.1. Reference Oil 17.2.2. Test water 17.2.3. The Sea Test 17.3. Test Description 17.3.1. The Rocky Shore Test 17.3.2. Rationale 17.3.3. Test Species 17.3.4. Test Description 17.3.5. Test Validity and Pass/Fail Assessment 17.4. Testing with Heavy Fuel Oils 17.5. The 2007 UK Scheme Review 17.5.1. Review and Improvement 17.5.2. Specific Issues 17.6. Conclusions References

613 613 615 615 616 617 618 618 619 619 620 620 620 626 627

Formulation Changes in Oil Spill Dispersants: Are They Toxicologically Significant?

629

Mark F. Kirby, Paula Neall, Jennifer Rooke, and Heather Yardley 18.1. Introduction 18.2. Materials and Methods 18.2.1. General Approach 18.2.2. Dispersants and Constituents 18.2.3. Toxicity Tests 18.2.4. Testing Schedule 18.3. Results 18.3.1. Inherent Toxicity of Constituent Chemicals and Dispersants 18.3.2. Toxicity of Reformulated Dispersants in the Sea Test 18.3.3. Toxicity of Reformulated Dispersants in the Rocky Shore Test 18.3.4. Inherent Toxicity of Reformulated Dispersants 18.4. Discussion 18.4.1. Do Formulation Changes Matter? 18.4.2. Sea Test 18.4.3. Rocky Shore Test 18.4.4. Are Specific Constituents of Concern? 18.4.5. Significance of Inherent Toxicity Changes of Formulations? Acknowledgments References

19.

Environment Canada’s Methods for Assessing Oil Spill Treating Agents Carl E. Brown, Ben Fieldhouse, Trevor C. Lumley, Patrick Lambert and Bruce P. Hollebone

629 630 630 631 631 633 633 633 634 635 635 638 638 639 639 640 641 641 642

643

Contents

19.1. 19.2.

Introduction Toxicity and Effectiveness of Treating Agents for Oil Spills 19.2.1. Dispersants 19.2.2. Shoreline-Washing Agents 19.2.3. Deemulsifiers and Emulsion Inhibitors 19.2.4. Herding Agents 19.2.5. Recovery Agents 19.2.6. Solidifiers and Gelling Agents 19.2.7. Biodegradation Agents 19.2.8. Sinking Agents 19.3. Approval for Use of Treating Agents in Canadian Waters 19.4. Challenges to Current Toxicity Test Protocols 19.4.1. Endocrine Disrupting Capacity 19.4.2. Genotoxicity 19.4.3. Sublethal Effects 19.5. Conclusions References

20.

The United States Environmental Protection Agency: National Oil and Hazardous Substances Pollution Contingency Plan, Subpart J Product Schedule (40 Code of Federal Regulations 300.900) William 20.1. 20.2. 20.3. 20.4. 20.5. 20.6. 20.7. 20.8.

21.

xv 643 645 645 653 657 658 658 658 659 661 662 662 664 664 665 666 667

673

J. Nichols Introduction Why Is There a Product Schedule? Authorities for a Product Schedule Information Requested from Manufacturers Agency Activities Practical Utility of the Data Authorities for Use Federal Agencies’ Role within the Regional Response Team 20.9. Does Listing Mean the Environmental Protection Agency Approves and Endorses a Product? 20.10. Conclusions 20.10.1. Proper Uses and Lessons Learned References

681 681 682 682

Surface-Washing Agents or Beach Cleaners

683

Merv Fingas and Ben Fieldhouse 21.1. Introduction to Surface-Washing Agents

683

673 674 675 675 679 679 680 680

xvi

22.

Contents

21.1.1. Motivations for Using Surface-Washing Agents 21.1.2. Surface Washing Agent Issues 21.1.3. Surface-Washing Agent Chemistry 21.2. Review of Major Surface-Washing Agent Issues 21.2.1. Effectiveness 21.2.2. Toxicity 21.3. Other Issues 21.3.1. Application 21.3.2. Dispersion with Higher Applied Energy 21.3.3. Assessment of the Use of Surface-Washing Agents References Appendix 21.1. Environment Canada’s Test Method Summary Method EPA Draft Protocol Summary Fieldhouse High-Energy Protocol

685 685 686 686 686 697 697 697 700 700 704 707 707 707 709 709 709

Review of Solidifiers

713

Merv Fingas and Ben Fieldhouse 22.1. Introduction to Solidifiers 22.1.1. Motivations for Using Solidifiers 22.1.2. Solidifier Issues 22.1.3. Solidifier Chemistry 22.2. Review of Major Solidifier Issues 22.2.1. Effectiveness 22.2.2. Toxicity 22.2.3. Biodegradation 22.3. Other Issues 22.3.1. Spill Size 22.3.2. Solidifier Use in Recent Times 22.3.3. Solidifiers or Sorbents 22.3.4. Potential for Sinking 22.3.5. Modeling Solidifier and Solidified Oil Behavior and Fate 22.3.6. Solidified Oil Stability 22.3.7. Fate of Unreacted Solidifier 22.3.8. Recovery of Solidified Oil 22.3.9. Solidification Time 22.3.10. Application Systems 22.3.11. Reduction of Flash Point 22.3.12. Assessment of the Use of Solidifiers 22.3.13. Disposal Methods or Recycling 22.4. Summary Acknowledgments References

713 713 714 714 717 717 728 728 728 728 729 729 729 729 729 729 729 730 730 730 730 730 730 731 731

Contents

Appendix 22.1. Testing Procedures from Environment Canada Solidifier Test Procedures Used in Early Years Oil Solidifier Effectiveness Test Used 1998 to Present Brief Description of the Test Equipment and Supplies Procedure Calculation

xvii 732 732 732 733 733 733 733

Part VII In-Situ Burning 23.

An Overview of In-Situ Burning 737 Merv Fingas 23.1. Introduction 737 23.2. An Overview of In-Situ Burning 737 23.2.1. The Science of Burning 737 23.2.2. Summary of In-Situ Burning Research and Trials 743 23.2.3. How Burns at Sea Are Conducted 750 23.2.4. Advantages and Disadvantages 755 23.2.5. Comparison of Burning to Other Response Measures 756 23.3. Assessment of Feasibility of Burning 758 23.3.1. Burn Evaluation Process 758 23.3.2. Areas Where Burning May Be Prohibited 758 23.3.3. Regulatory Approvals 763 23.3.4. Environmental and Health Concerns 765 23.3.5. Oil Properties and Conditions 793 23.3.6. Weather and Ambient Conditions 799 23.3.7. Burning in Special Locations 801 23.3.8. Burning on Land 806 23.3.9. Burning In or On Ice 809 23.4. EquipmentdSelection, Deployment, and Operation 811 23.4.1. Burning Without Containment 811 23.4.2. Oil Containment and Diversion Methods 814 23.4.3. Ignition Devices 834 23.4.4. Treating Agents 849 23.4.5. Support Vessels/Aircraft for At-Sea Burns 851 23.4.6. Monitoring, Sampling, and Analytical Equipment 852 23.4.7. Final Recovery of Residue 856 23.4.8. Equipment Checklist 858 23.5. Possible Spill Situations 858 23.6. Post-Burn Actions 870 23.6.1. Follow-Up Monitoring 870 23.6.2. Estimation of Burn Efficiency 873 23.6.3. Burn Rate 877 23.7. Health and Safety Precautions during Burning 878

xviii

Contents

23.7.1. Worker Health and Safety Precautions 23.7.2. Public Health and Safety Precautions 23.7.3. Establishing Safety Zones 23.7.4. Monitoring Burn Emissions Acknowledgments References

878 887 888 888 894 894

Part VIII Shoreline Countermeasures 24.

25.

Shoreline Countermeasures

907

Edward H. Owens 24.1. Introduction 24.1.1. Control At or Near the Source 24.1.2. Control on Water 24.1.3. Shoreline Protection Strategy 24.1.4. Shoreline Treatment 24.2. Shoreline Treatment Decision Process 24.3. Treatment Options 24.3.1. Natural Recovery 24.3.2. Physical Removal 24.3.3. In-Situ Treatment 24.4. Treatment by Shore Type 24.5. Waste Generation References

907 908 908 909 909 910 912 912 913 915 916 919 920

Automated Assessment and Data Management

923

Alain Lamarche 25.1. Introduction 25.2. Automated Processing and Data Management: Goals and Definition 25.2.1. Understanding the Use of Shoreline Assessment Data During a Response 25.2.2. The Nature of Shoreline Assessment Data 25.2.3. Practical Use of Shoreline Observations 25.3. Shoreline Observations Data Processing 25.3.1. Data Processing Organization 25.3.2. Responsibilities of the Shoreline Assessment Data Management Team 25.3.3. Data Management Tasks and Processes 25.3.4. Why and When to Establish a Shoreline Assessment Data Management Team 25.4. Assessment Automation Methods and Tools 25.4.1. Basic Tools 25.4.2. Combining Tools Within a Data Management Support System

923 924 924 924 927 929 929 931 935 939 939 940 944

xix

Contents

25.4.3. Information Distribution Shoreline Assessment Data Management Issues 25.5.1. Equipment Failure 25.5.2. Software Corruption 25.5.3. Overwhelming Amounts of Data 25.5.4. Conditions Unique to the Response References 25.5.

947 948 948 949 949 949 955

Part IX Submerged Oil 26.

Submerged Oil Jacqueline Michel 26.1. Introduction 26.2. Submerged Oil Characteristics 26.3. Review of Recent Submerged Oil Spills 26.3.1. M/V Athos I 26.3.2. T/B DBL-152 26.3.3. Lake Wabamun Spill 26.4. Submerged Oil Spill Response Methods and Recommendations for Future Work 26.4.1. Methods for Detection of Oil Suspended in the Water Column 26.4.2. Methods for Detection of Oil on the Bottom 26.4.3. Containment of Suspended Oil/Protection of Water Intakes 26.4.4. Containment of Submerged Oil on the Bottom 26.4.5. Recovery of Submerged Oil on the Bottom References

959 959 961 965 965 967 972 975 975 976 978 979 979 981

Part X Effects of Oil in the Environment 27.

Effects of Oil in the Environment Gary 27.1. 27.2. 27.3. 27.4. 27.5. 27.6. 27.7. 27.8. 27.9. 27.10. 27.11.

Shigenaka Introduction Some Definitions Size Matters: Seeps vs. Spills An “Equation” to Convey Toxic Impact Route of Exposure: The Anthrax Example Route of Exposure: Oil Oil Chemistry, Physical Behavior, and Oil Effects Freshwater/Saltwater Differences Tropical Environments Arctic Environments Ecological Effects of Oil Spills

985 985 987 989 991 999 1000 1003 1008 1010 1013 1014

xx

Contents

27.12. The Future of Oil Effects Science 27.13. Summary and Conclusions Acknowledgments Disclaimer References

1017 1019 1019 1019 1020

Part XI Contingency Planning and Command 28.

29.

Introduction to Oil Spill Contingency Planning and Response Initiation

1027

Merv Fingas 28.1. An Overview of Response to Oil Spills 28.2. Activation of Contingency Plans 28.3. Training 28.4. Structure of Response Organizations 28.5. Oil Spill Cooperatives 28.6. Private and Government Response Organizations

1027 1028 1029 1030 1030 1031

The Role of the International Tanker Owners Pollution Federation Limited

1033

Karen Purnell

30.

Safety Issues at Spills

1037

Quek Qiuhui 30.1. Introduction 30.2. Organization Structure 30.3. Health and Safety Risk Analysis/Risk Assessment 30.4. Air Monitoring 30.5. Site Safety and Health Plan 30.6. Different Types of Hazards on Site 30.7. Recommended Safety Procedures 30.7.1. Site Evaluation Process 30.7.2. Site Control Measures 30.7.3. Personal Protective Equipment 30.7.4. Excessive Noise 30.7.5. Heat Stress 30.7.6. Cold Stress 30.7.7. Monitoring Program 30.8. Emergency Procedures During a Response 30.8.1. Fire and Explosion 30.8.2. Hazardous Atmosphere/Hazardous Chemicals 30.8.3. Medical Emergencies 30.9. Other Issues

1037 1037 1038 1038 1043 1048 1049 1049 1050 1052 1052 1052 1054 1054 1054 1054 1058 1058 1059

Contents

30.9.1. Personnel Training 30.9.2. Volunteers 30.10. Conclusion Acknowledgments References

xxi 1059 1059 1062 1062 1062

Part XII Postassessment and Restoration 31.

32.

Natural Resource Damage Assessment

1067

Gary S. Mauseth and Heather Parker 31.1. Introduction 31.2. Regulatory Regimes 31.3. Objectives 31.4. Making the Public Whole 31.4.1. Injury Assessment 31.4.2. Interpretation of Restoration or Reinstatement 31.5. Alternative Sites 31.6. Use of Models 31.7. The NRDA Process in the United States 31.7.1. DOI CERCLA NRDA Regulations 31.7.2. NOAA NRDA Regulations Acronyms References

1067 1067 1069 1070 1071 1072 1075 1076 1077 1078 1079 1081 1082

Seafood Safety and Oil Spills

1083

Greg Challenger and Gary Mauseth 32.1. Introduction 32.2. Seafood Exposure to Oil 32.3. Spill Response and Seafood Safety Management 32.4. Seafood Safety Assessment: Reopening a Closed Fishery 32.5. Chemical Analytical Evaluation 32.6. Seafood Sensory Evaluation 32.7. Trends in Lifting Fishery Bans 32.8. Long-Term Implications of Oil Spills on Seafood References

1083 1085 1087 1090 1090 1092 1096 1098 1099

Part XIII Specific Case Studies 33.

The Torrey Canyon Oil Spill, 1967

1103

Robin J. Law 33.1. Case Study References

1103 1105

xxii

34.

35.

36.

Contents

The Ekofisk Bravo Blowout, 1977

1107

Robin J. Law 34.1. Case Study References

1107 1108

The Sea Empress Oil Spill, 1996

1109

Robin J. Law 35.1. Introduction 35.2. Mechanical Recovery at Sea 35.3. Dispersant Spraying at Sea 35.4. Shoreline Cleanup 35.5. Dispersant Use on Beaches 35.6. Impacts on Seabirds 35.7. Mortalities of Fish and Shellfish 35.8. Effects on Fish and Shellfish Stocks and Plankton 35.9. Contamination of Fish and Shellfish 35.9.1. Finfish 35.9.2. Crustacea 35.9.3. Whelks 35.9.4. Bivalve Mollusks 35.10. Removal of Fishery Restrictions 35.11. Conclusion References

1109 1110 1111 1112 1113 1113 1113 1114 1114 1114 1115 1115 1115 1115 1116 1116

The Braer Oil Spill, 1993

1119

Robin 36.1. 36.2. 36.3. 36.4.

37.

J. Law and Colin F. Moffat Introduction At-Sea and Shoreline Response Fate of the Braer Oil Impacts of the Braer Oil 36.4.1. On Land 36.4.2. On Seabirds 36.4.3. On Otters and Seals 36.4.4. On Commercial Fish and Shellfish 36.4.5. On Farmed Salmon 36.4.6. On Benthic Communities 36.4.7. On the Human Population 36.5. Conclusion References

1119 1119 1121 1121 1121 1121 1121 1123 1124 1125 1125 1125 1126

1991 Gulf War Oil Spill

1127

Jacqueline Michel 37.1. Review of the Spill References

1127 1131

Contents

38.

xxiii

Tanker SOLAR 1 Oil Spill, Guimaras, Philippines: Impacts and Response Challenges

1133

Ruth Yender and Katharina Stanzel 38.1. Incident Summary 38.2. Impact Summary 38.3. Shoreline Cleanup 38.4. Mangrove Cleanup and Recovery 38.5. Fisheries Impacts and Health Concerns 38.6. Summary Disclaimer References

1133 1134 1139 1143 1144 1145 1146 1146

Conversions Index

1147 1149

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Preface

Oil spill studies continue to evolve. While there are few books on the topic, there are regular conferences and symposiums which provide updates. This is the first book on the topic of oil spills for some time. As such, this book focuses on providing material that is more practical and somewhat introductory. While every attempt was made to include the essential material, there may be some gaps. The importance of many sub-topics changes with time and current spill situations. All material in this book, including introductions have been peer reviewed by at least two persons. The following peer reviewers are acknowledged (in alphabetical order): Carl Brown, Phil Campagna, Francois Charbonneau, Dagmar Schmidt Etkin, Ken Doe, Eric Gundlach, Kurt Hansen, Mike Kirby, Debra French McCay, Hugh Parker, Roger Percy, Karen Purnell, Doug Reimer, Gary Sergy, Debra Simecek-Beatty, Heidi Stout, Jordan Stout, Zhendi Wang, and Chun Yang. A special thanks goes out to the following reviewers who reviewed several papers (again in alphabetical order): Fred Beech, Leigh de Haven, Ben Fieldhouse, Anita George-Ares, Ron Goodman, Peter Lane, Robin Law, Bill Lehr, Jacqui Michel, and William Nichols. A special thanks goes out to the authors, many of whom put in their own time to complete their chapters. Their names appear throughout the text. Following this forward, I have a brief biography of each of them. I would also like to thank the many people who provided support and encouragement throughout this project, especially Meibing. I also thank Environment Canada and my former colleagues for their help and support. Environment Canada is acknowledged for permission to use materials and photos from my former employment.

xxv

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About the Contributors

Carl Brown Dr. Carl E. Brown is the Manager of the Emergencies Science and Technology Section, in the Water Science and Technology Directorate of Environment Canada. Dr. Brown has a doctorate degree in Physical Chemistry from McMaster University and a BSc in Laboratory Science from Ryerson Polytechnical University. Prior to joining Environment Canada, Dr. Brown was a research scientist on a Natural Sciences and Engineering Research Council (NSERC) Industrial Fellowship with Intera Information Technologies (now Intermap). Dr. Brown has post-doctoral experience as a research associate with the Organic Reaction Dynamics and the Laser Chemistry Groups at the Steacie Institute for Molecular Sciences, at the National Research Council of Canada and held a Canadian Government Laboratory Visiting Fellowship in Chemistry, with the Laser Chemistry Group, Division of Chemistry, National Research Council of Canada in Ottawa. His specialities include airborne oil spill sensor development, and the application of laser technologies to environmental problems. He has authored over 180 scientific papers and publications. Dr. Brown is the Chemical Science Cluster Leader for the CRTI (Chemical, Biological, Radiological, Nuclear, and Explosives Research and Technology Initiative) Program lead by Defence Research and Development Canada (DRDC) and Public Safety Canada. Dr. Brown was one of 24 scientists who recently completed the inaugural Scientists as Leaders program. Greg Challenger Greg Challenger is a Principal Marine Scientist for Polaris Applied Sciences, Incorporated in Seattle, Washington, U.S.A. Mr. Challenger is a marine ecologist by training and is involved in scientific support for oil spill and ship grounding response, natural resource injury assessment and development of habitat restoration programs. He has been a lead investigator for nearly 50 large vessel groundings, oil spills, and wreck removal operations in the Western Atlantic, Caribbean Sea, and Indo-Pacific Oceans. Dagmar Etkin Dagmar Schmidt Etkin has 35 years of experience in environmental analysis e 14 years investigating issues in population biology and ecological systems, and 21 years specializing in the analysis of oil spills. For the past 10 years, she has been president of Environmental Research Consulting (ERC), focusing on providing regulatory agencies and industry with sound scientific data and perspectives for responsible environmental decision-making. Dr. Etkin has a BA from University of Rochester, and M.A. and PhD from Harvard University. She is a member of the American Salvage

xxvii

xxviii

About the Contributors

Association, Maritime Law Association, and the UN Joint Group of Experts on the Scientific Aspects of Marine Protection. Merv Fingas Merv Fingas is a scientist focussing on oil and chemical spills. He was a spill researcher in Environment Canada for over 30 years and is currently working privately in Western Canada. Mr. Fingas has a PhD in Environmental Physics from McGill University, three masters degrees; Chemistry, Business, and Mathematics, all from University of Ottawa. His specialities include: spill dynamics and behaviour, spill treating agent studies, remote sensing and detection, and in-situ burning. He has over 750 papers and publications in the field. Dr. Fingas has been editor of the Journal of Hazardous Materials for 6 years. He has served on two committees on the U.S. National Academy of Sciences on oil spills including the recent ‘Oil in the Sea’. He is chairman of several ASTM and inter-governmental committees on spill matters. Bruce Hollebone Bruce Hollebone is a chemist with 14 years experience in the field of chemical and oil spill research and development. He has a PhD in Chemistry from the University of British Colombia. His research interests include: the fate and behaviour of oil and petroleum products in the environment, including simulation of spill behaviours in the laboratory; the development of new methods for physical and chemical analyses relevant to spills studies; environmental forensics for oil spill suspect-source identification; and environmental emergencies response. He currently works at the Oil Research Laboratory of Environment Canada. Mark Kirby Mark is an internationally recognised senior Ecotoxicologist with over 20 years experience working on studies pertaining to aquatic pollution. He has worked extensively on the toxicological impacts of oil and chemical spills and the assessment of appropriate methods of mitigation and has been involved in impact assessments in the UK from the Sea Empress to the MSC Napoli. He is a key advisor to the UK government and industry on the effects of oil and chemical spills in the marine environment and of any subsequent treatment actions (e.g. dispersants, sorbents etc.). Mark oversees the toxicological testing and approval of oil spill treatment products for use in UK waters and is the coordinator of a national initiative in the UK, PREMIAM (www.premiam.org), to implement improved post spill monitoring and impact assessment practices. He is first author of over 15 scientific papers and numerous reports in the field and continues to be actively involved in associated environmental research. Alain Lamarche Mr Lamarche is a recognized expert in spill response management systems. He has been involved in the analysis and management of environmental data since 1979. Mr. Lamarche has been responsible for the development and implementation of many computerized environmental decision support systems databases. He is also the original designer of the ShoreCleanÒ and ShoreAssess software, dedicated to the provision of Shoreline Cleanup Assessment Technique (SCAT) data management support, and

About the Contributors

xxix

Personal Digital Assistant (PDA) based geo-referenced field data acquisition tools. Mr. Lamarche has acted as a SCAT data manager and Geographical Information System (GIS) specialist during a number of oil spills, including: the Kure, New Carissa, Swanson Creek, Lake Wabamun, Westridge Line and Cosco Busan incidents. As principal of EPDS, Mr Lamarche is also responsible for all aspects of environmental software development projects including design, management, and implementation. Robin Law Robin Law is a chemist who joined Cefas (The UK Centre for Environment, Fisheries, and Aquaculture Science) in 1975. During the last 35 years he has been involved in the response and impact assessment activities following a number of major oil and chemical incidents, including the blowout on the Ekofisk Bravo platform, and from the oil tankers Amoco Cadiz, Eleni V, and Sea Empress, and the chemical tankers Ievoli Sun and Ece. Most recently, he designed and operated an environmental monitoring programme targeting oil and chemicals following the grounding of the container ship MSC Napoli on the south coast of the UK in 2007. Currently, he leads an emergency response team that advises UK government following oil and chemical spills at sea. Gary Mauseth Mr. Gary Mauseth has over 35 years of experience in the management and technical aspects of a wide variety of projects in the marine and freshwater environments. He has provided scientific support to vessel interests in over 90 spills, groundings, and natural resource damage assessment cases in the United States and its territories, as well as Canada, Mexico, the Caribbean and Mediterranean Seas, Micronesia, South America, and Europe. He has conducted research on the fate and effects of spilled oil, as well as the environmental effectiveness of response techniques, and has authored numerous publications and presentations on oil spill response, NRDA, and ecological restoration. Mr. Mauseth is a principal and President of Polaris Applied Sciences in Kirkland, Washington, USA. He has a Bachelor of Science in Biology from Whitman College in Walla Walla, Washington and a Master of Marine Biology from University of the Pacific, Pacific Marine Station, Dillon Beach, California. Jacqui Michel Dr. Jacqueline Michel is the President of Research Planning, Inc., and an internationally recognized expert in oil and hazardous materials spill planning and response. Her primary areas of expertise are in oil fates and effects, non-floating oils, shoreline cleanup, alternative response technologies, and natural resource damage assessment. Much of her expertise is derived from her role, since 1978, as part of the Scientific Support Team to the U.S. Coast Guard provided by the National Oceanic and Atmospheric Administration (NOAA). Under this role, she is on 24-hour call and provides technical support for 50-100 spill events per year. She leads shoreline assessment teams and assists in selecting cleanup methods to minimize the environmental impacts of the spill. She has evaluated and used a wide range of alternative response technologies, including surface washing agents, solidifiers, bioremediation

xxx

About the Contributors

agents, in-situ burning (mostly on wetlands and inland habitats), and methods to track and recover non-floating oils. William Nichols William (Nick) Nichols was born in Baltimore, Maryland and now lives in Ellicott City, Maryland. He has a Bachelor’s in Economics \Geography from Salisbury State University, Salisbury, Maryland and a Masters of Environmental Science from Johns Hopkins University, Baltimore. He has been an environmental scientist in the U.S. Environmental Protection Agency Office of Emergency Management (OEM) from 1997 and was U.S. National Contingency Plan Product Schedule Manager from 1998 to 2006. He is the national expert on chemical and biological oil spill countermeasures. He is also the OEM Tribal Coordinator from 2004 to the present. Ed Owens Dr. Owens is recognized internationally as an expert on oil spill shoreline cleanup and has worked on spill-related projects in the Arctic, North-South America, Africa, Russia, the Caspian, Australia, throughout South America, and in the Middle East. He has over 40 years experience providing technical and scientific support on oil response operations worldwide including: T/V Arrow (Canada), Hasbah 6 blowout (Arabian Gulf), T/V Exxon Valdez (USA), Arabian Gulf/Desert Storm (Bahrain, Qatar), Komineft pipeline (Russia), M/V Iron Baron (Australia), T/V Estrella Pampeana (Argentina), Desaguadero River (Bolivia), and M/V Cosco Busan spill (USA). Dr. Owens has conducted oil spill related missions as a United Nations Expert Consultant for the International Maritime Organization and as a consultant for the World Bank and the European Bank of Reconstruction and Development, and was a member of the U.S. National Academy of Science Oil Spill R&D Committee. Karen Purnell Dr Karen Purnell has been Managing Director of ITOPF since May 2009. She is a graduate of the Royal Society of Chemistry, with a PhD in Chemical Physics. Before joining ITOPF as a technical adviser in 1994, she worked on toxic waste management and environmental remediation in the nuclear industry and as a research chemist at several universities. Whilst at ITOPF, she has attended several major oil spills, including the sea empress (UK, 1996), prestige (Spain, 2002), and tasman spirit (Pakistan, 2003). Prominent amongst Karen’s achievements is the expansion of ITOPF’s capability to respond to spills of HNS (Hazardous & Noxious Substances). She has also worked closely with key U.S. agencies and the International Group of P&I Clubs on environmental issues. Dr Purnell has established a constructive dialogue with shipowners and is highly respected in the maritime community. Qiuhui Quek Qiuhui Quek holds a Bachelor’s degree in Environmental Engineering and has attended a number of responses in Australia, India, Indonesia, Korea, Libya, and Singapore. She has also delivered several workshops for Oil Spill Response members in various countries in the region. Qiuhui recently completed a secondment in Southampton as part of the Duty Manager rotation program. She also presented papers in international conferences such

About the Contributors

xxxi

as IOSC, Interspill, and SPE. Qiuhui has since left Oil Spill Response and is working in the HSE Management Unit of A)STAR Research Institutes. Gary Shigenaka Gary Shigenaka is the lead marine biologist with the Emergency Response Division (ERD) of the NOAA, based in Seattle. Gary received both his bachelor of science and masters degrees from the University of Washington in Seattle. As a graduate student, he served as a Knauss Sea Grant Policy Fellow in Washington D.C., and was awarded the Donald L. McKernan prize for outstanding marine affairs thesis. He has provided biological and shoreline assessment support during spills of oil and hazardous chemicals across the country and internationally over the last two decades. Gary was part of the early scientific mobilization for the Exxon Valdez oil spill in 1989, and continues to monitor the long-term effects in Prince William Sound. He also oversees other research initiatives for NOAA/ERD designed to improve oil spill impact understanding, and also to develop and improve biological tools for response and assessment. He has published numerous articles on the science and applied aspects of his spill-related research. Debra Simecek-Beatty Debra Simecek-Beatty has been a physical scientist for the NOAA’s Emergency Response Division for 25 years. She has a Masters degree in Marine Affairs from the University of Washington. During an emergency response, she is responsible for providing estimates of the movement and behavior of the spill. This includes collecting visual observations, remote sensing information, wind and current data, and computer modeling output to form an analysis. In addition, she is responsible for interfacing with local experts (i.e., meteorologist, academia, researchers) in formulating the trajectory analysis. Ruth Yender Ruth Yender is a marine ecologist with NOAA’s Office of Response and Restoration, based in Seattle, Washington. As NOAA’s Scientific Support Coordinator for the U.S. Pacific Northwest and Pacific Islands regions, Ruth provides remote and on-scene support to the U.S. Coast Guard during responses to oil and hazardous materials spills. Since joining NOAA in 1992, she has responded to more than 100 oil and chemical spills in the U.S. and internationally. Ruth also participates in spill response planning, conducts training for responders, and writes response technical guides.

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Part I

Introduction and the Oil Spill Problem

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Chapter 1

Introduction Merv Fingas

Chapter Outline 1.1. Introduction 1.2. A Word on the Frequency of Spills

3 4

1.1. INTRODUCTION Major oil spills attract the attention of both the public and the media. In past years, this attention created a global awareness of the risks of oil spills and the damage they do to the environment. In recent years, there have been fewer major spill incidents, as noted by Dagmar Etkin in Chapter 2. The public usually becomes aware of major spills, but generally does not recognize that spills are a daily fact of life. Oil is a necessity in our industrial society and a major element of our lifestyle. Most of the energy used in much of the developed world is for transportation that runs on oil and petroleum products. As current energy usage trends show, this situation is not likely to change much in the future. Industry uses oil and petroleum derivatives to manufacture such vital products as plastics, fertilizers, and chemical feedstocks, all of which will continue to be required in the future. In fact, production and consumption of oil and petroleum products are increasing worldwide, and the risk of oil pollution is increasing accordingly. The movement of petroleum from the oil fields to the consumer involves as many as 10 to 15 transfers between many different modes of transportation, including tankers, pipelines, railcars, and tank trucks. Oil is stored at transfer points and at terminals and refineries along the route. Accidents can occur during any of these transportation steps or storage times. Fortunately, in the past few years the actual number of spills has decreased. Obviously, an important part of protecting the environment is ensuring that there are as few spills as possible. Both government and industry are working to reduce the risk of oil spills by introducing strict new legislation and stringent operating codes. Industry has invoked new operating and maintenance Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10001-2 Copyright Ó 2011 Elsevier Inc. All rights reserved.

3

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Introduction and the Oil Spill Problem

procedures to reduce accidents that lead to spills. Intensive training programs have been developed to reduce the potential for human error. Despite these measures, spill experts estimate that 30 to 50% of oil spills are either directly or indirectly caused by human error, with 20 to 40% of these incidents caused by equipment failure or malfunction. There are also many deterrents to oil spills, including government fines, loss of reputation, and high cleanup costs. In Canada, it costs an average of $20 to clean up each liter (about 1/4 gallon) of oil spilled. In the United States, these costs average about $100 per liter spilled, whereas the average cost of cleanup worldwide ranges from $20 to $200 per liter, depending on the type of oil and where it is spilled. Cleaning up oil on shorelines is usually the most expensive cleanup process.

1.2. A WORD ON THE FREQUENCY OF SPILLS Smaller oil spills are a frequent occurrence in the world, particularly because of the heavy use of oil and petroleum products in our daily lives. Canada uses about 260,000 tons of these products every day; the United States uses about 10 times this amount, and, worldwide, about 10 million tons are used per day. Most domestic oil production in Canada comes from approximately 350,000 oil wells in Alberta and Saskatchewan. There are 22 oil refineries in Canada, 5 of which are classified as large. Canada imports about 100,000 tons of crude oil or other products per day but exports about 600,000 tons per day, mostly to the United States. In the United States, more than half of the approximately 3 million tons of oil and petroleum products used daily is imported, primarily from Canada, Africa, Saudi Arabia, and other Arabic countries. About 40% of the daily demand in the United States is for automotive gasoline, and about 15% is for diesel fuel used in transportation. About 40% of the energy used in the United States comes from petroleum, 35% from natural gas, and 24% from coal. Spill statistics are collected by a number of agencies around the world. In Canada, provincial offices collect data, and Environment Canada maintains a database of spills. In the United States, the Coast Guard handles a database of spills into navigable waters, while state agencies keep statistics on spills on land which are sometimes gathered into national statistics. The Minerals Management Service (MMS) in the United States maintains records of spills from offshore exploration and production activities. It can sometimes be misleading to compare oil spill statistics, however, because different countries use different methods to collect the data. In general, statistics on oil spills are not easily obtainable, and any data set should be viewed with caution. Determining or estimating the spill volume or amount is the most difficult aspect of data collection. For example, in the case of a vessel accident, the exact volume in a given compartment may be known before the accident, but the remaining oil may have been transferred to other ships

Chapter | 1

Introduction

5

immediately after the accident. Some spill accident data banks do not include the amounts burned, if and when that occurs, whereas others include all the oil lost by whatever means. Sometimes the exact character or physical properties of the oil lost are not known, thereby leading to different estimates of the amount lost. Spill data are often collected for purposes other than future improvement of the spill response. Reporting procedures vary in different jurisdictions and organizations, such as government or private companies. Minimum spill amounts that must be reported according to federal regulations in Canada and the United States vary from 400 to 8,000 liters (100 to 2000 gals), depending on the product spilled. Spill statistics compiled in the past are less reliable than those based on more recent data because few agencies or individuals collected spill statistics before about 1975. Today, although techniques for collecting statistics are continually being improved, the resources allocated for this purpose have been reduced. The number of spills reported also depends on the minimum size or volume of the spill. In both Canada and the United States, most oil spills reported total more than 4000 L (about 1000 gals). In Canada, about 12 such oil spills take place every day, of which only about one of these spills is into navigable waters. These 12 spills amount to about 40 tons of oil or petroleum product. In the United States, there are about 25 spills per day into navigable waters and an estimated 75 spills on land. Despite the apparently large number of spills, only a small percentage of oil used in the world is actually spilled. There are proportionately more spills into navigable waters in the United States than in Canada because more oil is imported by sea and more fuel is transported by barge. In fact, the largest volume of oil spilled in U.S. waters comes from barges, while the largest number of spills is from vessels other than tankers, bulk carriers, or freighters. In Canada, most spills take place on land, and this accounts for a high volume of oil spilled. Pipeline spills account for the highest volume of oil spilled. In terms of the actual number of spills, most oil spills happen at petroleum production facilities, wells, production collection facilities, and battery sites. On water, the greatest volume of oil spilled comes from marine or refinery terminals, although the largest number of spills is from the same source as in the United Statesdvessels other than tankers, bulk carriers, or freighters. The public has the wide misconception that oil spills from tankers are the primary source of oil pollution in the marine environment. Although some of the large spills are indeed from tankers, these spills still make up less than about 5% of all oil pollution on the seas. The sheer volume of oil spilled from tankers and the high profile given these incidents in the media have contributed to this misconception. In fact, as stated earlier, half of the oil spilled in the seas is the runoff of oil and fuel from land-based sources rather than from accidental spills. In conclusion, it is important to study spill incidents from the past to learn how the oil has affected the environment, what cleanup techniques work, and what improvements can be made, as well as to identify the gaps in technology.

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Chapter 2

Spill Occurrences: A World Overview Dagmar Schmidt-Etkin

Chapter Outline 2.1. Introduction 2.2. Executive Summary

7 8

2.3. Overview of Spill Occurrences

8

2.1. INTRODUCTION Asked to picture an oil spill, most people envision a large tank ship (tanker) grounded on a large rock or reef after having gone off-course in a storm or due to navigational errors. Depending on one’s frame of reference and nationality, this might be the Exxon Valdez incident, the Hebei Spirit spill, or perhaps the Prestige spill. Oil-coated beaches, dead birds, angry fishermen, and massive cleanup efforts complete the picture. Although these types of “catastrophic” spill incidents do indeed occur occasionally and receive considerable media coverage, they are, fortunately, relatively rare events. Much more commonly, oil spills are much smaller in scope. On any given day, hundreds, if not thousands, of spills are likely to occur worldwide in many different types of environments, on land, at sea, and in inland freshwater systems. The spills are coming from the various parts of the oil industrydfrom oil exploration and production activities, from transport of that oil by tank ships, pipelines, and railroad tankcars to the refineries, and from the refineries where the oil is refined to create the many types of fuels that are then transported by pipeline, rail, truck, or tank vessel to the consumers of that oil. Consumption-related spillage comes from manufacturing facilities, nontank vessels that carry oil only as fuel and for machinery, tanker trucks bringing oils to service stations and heating oil tanks, and many miscellaneous sources. The spills occur because of structural failures, operational errors, weather-related Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10002-4 Copyright Ó D.S. Etkin 2011.

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Introduction and the Oil Spill Problem

events, earthquakes, human errors and negligence, and even vandalism or terrorism. The spills involve many different types of oil ranging from various types of crude oil to a large array of refined products, from heavy persistent fuels to lighter, less persistent, but very toxic lighter fuels. Because each spill occurs in a different location under different circumstances of oil type and volume, proximity to sensitive resources, season, weather effects, and currents, each spill is a relatively unique event in terms of impacts, damages, and response challenges.

2.2. EXECUTIVE SUMMARY Worldwide oil spillage rates have decreased dramatically since the 1960s and 1970s, from about 635,000 tons annually to about 300,000 tons per year from all sources, not counting the anomalous intentional spillage associated with the 1991 Gulf War, which amounted to over 82 million tons on land and at sea. The largest sources of oil spills in the last two decades have been related to oil transportation by tank ships (tankers) or through pipelines. Oil inputs from spills and other chronic discharge sources, such as urban runoff, refinery effluents, and vessel operational discharges, currently total about 1.2 million tons worldwide annually. While most spills are relatively small and cause localized impacts, occasionally very large spills occur that cause significant environmental and socioeconomic damages. Despite significant progress in reducing spillage through a variety of technological and regulatory prevention measures along with better industry practices, the risk for significant oil spills remains. A more detailed analysis of oil spillage in the United States, for which there are more accurate data than many other parts of the world, reveals that during the decade of 1998e2007, inland pipelines spilled an average of nearly 11,000 tons annually, with the next largest source being refineries, which spilled 1,700 tons. Inland tanker truck spills amounted to 1,300 tons annually. Tank ships only spilled an average of 500 tons annually during this decade. Nevertheless, the risk for large spills from tank ships, facilities, and offshore oil exploration and production, all of which contain large volumes of oil, remains a concern for contingency planners and spill responders.

2.3. OVERVIEW OF SPILL OCCURRENCES 2.3.1. Natural Oil Seepage Oil slicks on water and oiled shorelines are not new phenomena. A considerable amount of crude oil is discharged each year from “natural seeps”dnatural springs from which liquid and gaseous hydrocarbons (hydrogen-carbon compounds) leak out of the ground. Oil seeps are fed by natural underground

Chapter | 2

Spill Occurrences: A World Overview

9

accumulations of oil and natural gas. Oil from submarine (and inland subterranean) oil reservoirs comes to the surface each year, as it has for millions of years due to geological processes. Natural discharges of petroleum from submarine seeps have been recorded throughout history going back to the writings of Herodotus1 and Marco Polo.2 Archaeological studies have shown that products of oil seeps were used by Native American groups living in California, including the Yokuts, Chumash, Achomawi, and Maidu tribes, well before the arrival of European settlers.3 In recent times, the locations of natural seeps have been used for exploration purposes to determine feasible locations for oil extraction. Regional assessments of natural seepage have been conducted in some locations, particularly nearshore in California,4-7 the Indian Ocean,8-10 and the Gulf of Mexico.11 The most comprehensive worldwide assessment of natural seepage is still the study conducted by Wilson et al.12 Even the two more recent international assessments of oil inputs into the sea13 relied heavily on the estimates of natural oil seepage conducted by Wilson et al.,12 having found no more recent comprehensive studies. While industry studies have been conducted for the purpose of determining potential locations for oil exploration and production using various forms of increasingly sophisticated technology, no results have been openly published in the scientific or technical literature. Natural seeps are of such great magnitude that, according to the prominent geologists Kvenvolden and Cooper,14 “natural oil seeps may be the single most important source of oil that enters the ocean, exceeding each of the various sources of crude oil that enters the ocean through its exploitation by humankind.” Assessments of natural oil seepage involve few actual measurements, though certain seep locations along the Southern California coast of the Pacific Ocean have been studied to some extent. Natural seep studies have also included identification of hydrothermically sourced hydrocarbons (especially polycyclic aromatic hydrocarbons) in sediments. The most well-known studies have relied on estimation methodologies based on field data, observations, and various basic assumptions. Wilson et al. estimated that total worldwide natural seepage ranged from 0.2 to 6.0 106 tonsy annually, with the best estimate being 0.6 106 tons, based largely on observations of seepage rates off California and western Canada.12 Estimates of the areas of ocean with natural seeps are shown in Table 2.1, and estimates of seepage rates by ocean are shown in Table 2.2. y

Oil measurements are in metric tons (tons). Within the industry, oil is often measured in barrels (equivalent of 42 U.S. gallons or 159 liters), roughly equal to one-seventh of a ton, depending on specific gravity. Conversion between tons (weight) and barrels (volume) is per the formula: tons ¼ 0.173 barrels specific gravity.

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Introduction and the Oil Spill Problem

TABLE 2.1 Seepage-prone Areas of the World’s Oceans (based on Wilson et al.12) Number of 1,000 Square Kilometers

Ocean

High-potential Seepage

Moderate-potential Seepage

Low-potential Seepage

Pacific

1,943

9,285

4,244

Atlantic

1,303

10,363

11,248

Indian

496

7,928

3,010

Arctic

0

5,636

2,456

Southern

0

486

458

3,741

33,697

21,416

Total

Wilson et al. based their estimates on five basic assumptions: More seeps exist in offshore basins than have been observed; factors that determine seepage rates in a particular area are related to general geological structural type and stage of sedimentary basin evolution; seepage is dependent on the area of exposed rock rather than on rock volume; most marine seeps are clustered at continental margins; and seepage rates are lognormally distributed.12

TABLE 2.2 Summary of World Seepage Rates (based on Wilson et al.12) Estimated Oil Seepage (106 tons per year) Ocean

Case I, P16z

Case II, P1.0x

Case III, P0.3**

Pacific

2.83 106

2.69 105

0.689 105

Atlantic

2.06 106

1.96 105

5.04 104

Indian

9.30 105

8.85 104

2.28 104

Arctic

2.14 105

2.30 103

5.20 103

Southern

1.88 104

1.74 103

4.51 102

Total

6.05 3 106

0.558 3 105

0.148 3 106

z

Probability percentile 16 with a worldwide estimate of 6 106 tons annually, likely a high estimate. Probability percentile 1.0 with a worldwide estimate of 0.6 106 tons annually. **Probability percentile 0.3 with a worldwide estimate of 0.2 106 tons annually, likely a minimal estimate. x

Chapter | 2

Spill Occurrences: A World Overview

11

Kvenvolden and Harbaugh15 concluded that the minimal worldwide estimate (0.2 106 tons annually) from the Wilson et al.12 study is most likely correct and that an error margin of an order of magnitude above and below this value should be applied (i.e., 0.02 106 to 2.0 106 tons annually). Their theory was based on a reduced value for the assumed and known oil resources that would be available for seepage. There is some evidence that seepage rates are decreasing in some locations, such as those near Coal Point, off Santa Barbara, California.16 In a 2003 National Research Council (NRC) study, a worldwide estimate of natural seepage into the marine environment of between 0.02 106 to 2.0 106 tons annually was made, with a “best estimate” of 600,000 tons.17 These estimates were made based on the Kvenvolden and Harbaugh15 reassessment of the estimates made by Wilson et al.,12 as well as an acceptance of the original estimates of Wilson et al.,12 resulting from a “new appreciation” for the magnitude of natural seepage, particularly in the Gulf of Mexico. Relying largely on the Wilson et al.12 and Kvenvolden and Harbaugh15 studies, the 2007 Joint Group of Experts on Scientific Aspects of Marine Protection (known as GESAMP) study on oil inputs into the marine environment included an estimate of the range of natural seepage as 0.22.0 106 tons per year, with a best estimate of 600,000 tons per year.13 Natural seeps often release oil sporadically in relatively small amounts, but occasionally release larger amounts that can have the same environmental impacts as crude oil spills from tankers or other sources. But while natural seeps have had impacts on the marine and terrestrial environment since prehistoric times, it was not until the occurrence of several larger anthropogenic oil spills in the late 1960s, which coincided with a greater public awareness of general environmental issues, that concern over oil pollution came to the forefront.

2.3.2. Historical Concern Over Oil Pollution When the tanker Torrey Canyon spilled 130,000 tons of crude oil off the western coast of the UK in March 1967, killing 15,000 seabirds and oiling nearly 300 kilometers of English and French coastline, there was a large public outcry. The environmental damage from this spill was multiplied by the use of highly toxic first-generation dispersant chemicals in the response. The Torrey Canyon spill was not the first oil tanker spill by any means. A large number of oil tankers were torpedoed and sunk during World War II. According to Campbell et al., during the first six months of 1942 alone, a total of 484,200 tons of oil were released from torpedoed tankers within 90 kilometers of the eastern U.S. coast.18 This came to about one tanker spill of about 20,000 tons per week over six months. Cleanup efforts consisted of burning incidental to the torpedoing and minimal cosmetic actions on swimming beaches. While the occurrence of these incidents during wartime may explain the

12

PART | I

Introduction and the Oil Spill Problem

relatively low concern about environmental damage from the spilled oil, there was, arguably, a general lesser awareness of environmental protection in these times as well. The Torrey Canyon spill in 1967 was notable in that when it occurred, it is the largest spill to date. The tanker’s capacity had recently been increased to hold 130,000 tons of oil cargo. Subsequently, there were at least five significantly larger worst-case discharge (complete cargo loss) tanker spills, as well as several other large spills associated with oil wells and pipelines. Following on the 1967 Torrey Canyon incident, the 1969 Union Alpha Well 21 blowout off Santa Barbara, California, which released 14,300 tons of crude oil, is often credited with being the impetus for the environmental movement in the United States, as well as for the establishment of the federal Environmental Protection Agency (EPA).19 In the 1970s, other significant oil spills around the world brought greater attention to the problem on an international scaledthe tanker Metula (Chile in 1974), the tanker Urquiola (Spain in 1977), the tanker Amoco Cadiz (France in 1978), the largest tanker spill of all time, Atlantic Empress (Trinidad and Tobago/Barbados in 1979), and the largest nonewar-related spill in historydthe Ixtoc I well blowout (Gulf of Mexico in 1979).20 The largest oil spills in history are listed in Table 2.3. The 1989 tanker Exxon Valdez spill in Alaska is perhaps the most notorious spill incident, though it is by no means the largest. The spillage of over 37,000 tons of Alaskan crude oil into what was considered to be a “pristine” location, Prince William Sound, precipitated the most expensive and the lengthiest spill response and damage settlements in history. Its repercussions were felt worldwide, resulting in the passage of significant spill prevention and liability legislation in the United Statesdthe Oil Pollution Act of 1990 (OPA 90)das well as international conventions on spill prevention that included such measures as the requirement for double-hulls on tankers by 2015 and increased financial liability. The significant financial consequences for tanker owners and operators as a result of the Exxon Valdez spill and the spiller liability inherent in subsequent regulations brought the consequences for spills to an unprecedented level. The financial risk associated with large spills may have had as much impact on spill prevention as any actual preventive measures, such as double-hulls on tankers.

2.3.3. Sources of Oil Spills and Patterns of Spillage Spills occur around the worlddanywhere that oil is produced, transported, stored, or consumed. The vast majority of spills are relatively small. As shown in Figure 2.1, 72% of spills are 0.003 to 0.03 ton or less. The total of amount of these small spills comes to 0.4% of the total spillage. The largest spills (over 30 tons) make up 0.1% of incidents but involve nearly 60% of the total amount spilled. Naturally, the relatively rare large spill incidents get the most public attention owing to their greater impact and visibility, though spill size itself is

Chapter | 2

13

Spill Occurrences: A World Overview

TABLE 2.3 Largest Oil Spills in History Worldwide Environmental Research Consulting (ERC data)** Date

Source Name*

Location

Tons

10-Mar-1991

700 oil wells

y

Kuwait

71,428,571

20-Jan-1991

Min al Ahmadi Terminalyz

Kuwait

857,143

3-Aug-2000

oil wells

Russia

700,000

3-Jun-1979

Ixtoc I well

Mexico

476,190

Iraq

377,537

Uzbekistan

299,320

Trinidad/Tobago

286,354

Russia

285,714

y

1-Feb-1991

Bahra oil fields

2-Mar-1990

oil well x

19-Jul-1979

T/V Atlantic Empress

25-Oct-1994

Kharyaga-Usinsk Pipeline y

4-Feb-1983

No. 3 Well (Nowruz)

Iran

272,109

6-Aug-1983

T/V Castillo de Bellver

South Africa

267,007

16-Mar-1978

T/V Amoco Cadiz

France

233,565

10-Nov-1988

T/V Odyssey

Canada

146,599

11-Apr-1991

T/V Haven

Italy

144,000

1-Aug-1980

D-103 concession well

Libya

142,857

6-Jan-2001

pipeline

Nigeria

142,857

Kuwait

139,690

19-Jan-1991

yz

T/V Al Qadasiyah y

19-Jan-1991

T/V Hileen

Kuwait

139,690

18-Mar-1967

T/V Torrey Canyon

United Kingdom

129,857

19-Dec-1972

T/V Sea Star

Oman

128,891

23-Feb-1980

T/V Irenes Serenade

Greece

124,490

yz

19-Jan-1991

T/V Al-Mulanabbi

Kuwait

117,239

7-Dec-1971

T/V Texaco Denmark

Belgium

107,143

19-Jan-1991

T/V Tariq Ibn Ziyadyz

Kuwait

106,325

20-Aug-1981

storage tanks

Kuwait

106,003

yz

26-Jan-1991

Min al Bakar Terminal

Kuwait

100,000

15-Nov-1979

T/V Independentza

Turkey

98,255

11-Feb-1969

T/V Julius Schindler

Portugal

96,429

(Continued )

14

PART | I

Introduction and the Oil Spill Problem

TABLE 2.3 Largest Oil Spills in History Worldwide Environmental Research Consulting (ERC data)**dcont’d Date

Source Name*

Location

12-May-1976

T/V Urquiola

Spain

95,714

25-May-1978

No. 126 Well/pipeline

Iran

95,238

28-Mar-1995

pipeline

Nigeria

90,000

5-Jan-1993

T/V Braer

United Kingdom

85,034

Kuwait

83,897

yz

Tons

1-Mar-1991

pipeline

29-Jan-1975

T/V Jakob Maersk

Portugal

82,503

6-Jul-1979

storage tank (Tank #6)

Nigeria

81,429

19-Nov-2002

T/V Prestige

Spain

77,000

3-Dec-1992

T/V Aegean Sea

Spain

74,490

6-Dec-1985

T/V Nova

Iran

72,626

15-Feb-1996

T/V Sea Empress

United Kingdom

72,361

19-Dec-1989

T/V Khark 5

Morocco

70,068

27-Feb-1971

T/V Wafra

South Africa

68,571

11-Dec-1978

fuel storage depot

Zimbabwe

68,027

26-Apr-1992

T/V Katina P.

South Africa

66,700

12-Jun-1978

Sendai Oil Refinery

Japan

60,204

6-Dec-1960

T/V Sinclair Petrolore

Brazil

60,000

7-Jan-1983

T/V Assimi

Oman

53,741

9-Nov-1974

T/V Yuyo Maru No. 10

Japan

53,571

28-May-1991

T/V ABT Summer

Angola

51,020

22-May-1965

T/V Heimvard

Japan

50,000

31-Dec-1978

T/V Andros Patria

Spain

49,660

30-Jan-1991

T/V Ain Zalah yz

Kuwait

49,543

13-Jun-1968

T/V World Glory

South Africa

48,214

13-Jan-1975

T/V British Ambassador

Japan

48,214

9-Dec-1983

T/V Pericles GC

Qatar

47,619

9-Aug-1974

T/V Metula

Chile

47,143

Chapter | 2

15

Spill Occurrences: A World Overview

TABLE 2.3 Largest Oil Spills in History Worldwide Environmental Research Consulting (ERC data)**dcont’d Date

Source Name*

Location

Tons

1-Jun-1970

T/V Ennerdale

Seychelles

46,939

7-Dec-1978

T/V Tadotsu

Indonesia

44,878

29-Feb-1968

T/V Mandoil

United States

42,857

10-Jun-1973

T/V Napier

Chile

38,571

13-Mar-1994

T/V Nassia

Turkey

38,500

26-Aug-1979

T/V Patianna

United Arab Emirates

38,000

11-Jun-1972

T/V Trader

Greece

37,500

24-Mar-1989

T/V Exxon Valdez

United States

37,415

29-Dec-1980

T/V Juan Antonio Lavalleja

Algeria

37,279

21-Oct-1994

T/V Thanassis A.

Hong Kong

37,075

22-Apr-1988

T/V Athenian Venture

Canada

36,061

7-Feb-1977

T/V Borag

Taiwan

35,357

Mar-1986

Pemex Abkatun 91

Mexico

35,286

6-Feb-1976

T/V St. Peter

Colombia

35,100

*“T/V” ¼ “tank vessel” and refers to tank ships or tankers. **Ended in January 2010. y War-related intentional spillage. z Several intentional spills occurred nearly simultaneously during the 1991 Gulf War. They are often aggregated into one large “spill.” In this list, the individual spill sources are separated. x T/V Atlantic Empress spilled 145,250 tons of oil off Trinidad and Tobago on 19 July 1979, then another 141,000 tons while under tow off Barbados.

not a direct measure of damage. Location and oil type are extremely important in determining the degree of environmental and socioeconomic damage. Oil spills and discharges* can occur at any point in the “life cycle” of petroleumdduring oil exploration and production; transport by vessel, pipeline, railroad, or tanker truck; refining; storage, consumption or usage as fuel or as raw material for manufacturing; or waste disposal. The regional and national patterns of spillage depend on the oil-related activities in those *

A “spill” is a discrete event in which oil is accidentally or, occasionally, intentionally released. A “discharge” is a legal permitted release of oil (usually in a highly diluted state in water) as part of normal operations.

16

PART | I

Introduction and the Oil Spill Problem

% Total Spills 80%

71.9%

70%

# spills

60%

amount

50% 39.5%

40% 30%

22.8%

22.2%

20.3%

20% 6.2%

10% 0%

11.7%

0.4%

0.003

1.6%

0.03

4.3%

0.3

1.6%

3

0.3%

3,000

0.1%

30

0.0%

300

Spill Size (tonnes) FIGURE 2.1 Size classes of U.S. marine oil spills, 1990e1999 (ERC data).

locations, the amount of oil handled, and the degree to which oil prevention measures have been implemented and enforced. Overall, oil spillage has decreased significantly in the United States and internationally due to the implementation and enforcement of prevention measures as well as more responsible operations on the part of the shipping and oil industries.13,17,21,22 In the 1970s, an estimated 6.3 million tons of oil spilled into marine waters from all sources, excluding war-related incidents.22 By the 1980s, an estimated 3.8 million tons of oil spilled worldwide, a 40% reduction since the decade 1988e1997. Spillage reduced another 20% by the 1990s. These reductions in spillage are all the more remarkable considering the increases in production, shipping, and handling of oil during this time period (Table 2.4). In a series of studies that estimated total oil inputs into the marine environment from spills, as well as from operational discharges* from shipping and other sources, especially urban runoff,y a definitive trend of input reduction is apparent (Table 2.5). It is important to note that some of the variations between the studies are due to differences in methodology rather than to actual differences in inputs. *

A legal permitted release of oil (usually in a highly diluted state in water) as part of normal operations. y “Urban runoff” is the accumulation of drops of oil that leak from automobiles, trucks, and other vehicles, as well as small chronic spillages that occur from other land-based sources. The oil washes off into storm sewers, culverts, and other waterways into streams and rivers that enter marine waters. Because the exact source of this spillage cannot be pinpointed, it is termed nonpoint source pollution.

Chapter | 2

17

Spill Occurrences: A World Overview

TABLE 2.4 Annual Worldwide Marine Oil Spillage (ERC Data) Estimated Average Annual Tons Spilledy Source Type

1970s

1980s

1990s

428,646

190,180

126,743

2,735

23,811

10,248

Pipelines

59,087

36,744

85,664

Facilities

66,067

58,047

35,655

Offshore Exploration/ Production

69,111

68,099

38,351

9,241

1,775

3,905

634,887

378,656

300,546

Tank Vessels Nontank Vessels

Unknown/Other Total y

Excluding war-related spills.

The tracking of oil spills is generally conducted by those authorities involved in initiating emergency spill response operations, such as Coast Guard agencies or state and local governments. The accuracy of reporting, particularly of smaller spills, varies considerably from one jurisdiction to another. There have been increases in the reporting of increasingly smaller spills, though not necessarily in the actual incidence of such spills, which reflects broader public awareness of spills and greater concern about and responsibility for these incidents by spillers. As larger spills become increasingly rarer, it is important that contingency planners and spill responders maintain preparedness for these large spills owing to the potential damages associated with them.22,23 A detailed recent overview of oil spills in the United States is presented here based on Environmental Research Consulting (ERC) data, along with analytical results from some past international studies on oil spills.24-28

2.3.4. Spillage from Oil Exploration and Production Activities During the years 1998e2007, an estimated 182 tons of crude oil spilled annually from offshore exploration and production platforms into U.S. waters. An additional 373 tons spilled annually from pipelines associated with offshore oil production, for a total of 555 tons per year. This represents a nearly 66% reduction in spillage since 1988e1997, and an 87% reduction in spillage since the 1970s (1969e1977). Oil spillage from offshore platforms in U.S. Outer Continental Shelf (OCS) and state waters is shown in Figure 2.2 for 1969e2007.

TABLE 2.5 Estimated Worldwide Oil Inputs Based on Various Studies 18

Oil Input Estimates (Tons) Source Natural Seeps Municipal/Industrial Urban Runoff Coastal Refinery Other Coastal

Tanker Accidents Other Shipping

Atmospheric

zzz

Offshore Expl/Prod TOTAL z

1981**

1981yy

1990zz

1997xx

2000***

600,000

600,000

300,000

200,000

258,500

600,000

600,000

2,700,000

2,250,000

1,480,000

1,230,000

1,175,000

114,900

156,900

2,500,000

2,100,000

1,430,000

1,080,000

e

n/a

140,000

200,000

e

e

100,000

e

112,500

4,900

e

150,000

50,000

50,000

e

2,400

12,000

2,130,000

1,100,000

1,440,000

1,420,000

564,000

389,000

413,100

300,000

300,000

390,000

400,000

e

157,900

100,000

yyy

7,100

750,000

200,000

340,000

320,000

e

586,500

1,080,000

600,000

710,000

700,000

e

225,800

306,000

600,000

600,000

300,000

300,000

305,000

68,000

24,700

80,000

60,000

50,000

50,000

47,000

19,750

38,000

10,940,000

7,960,000

6,490,000

5,850,000

2,349,500

2,246,750xxx

1,802,700

[24] [25] **[26] yy [27] zz [28] xx [13] ***[17] yyy Includes 53,000 tons from small-craft activity. zzz Atmospheric deposition of petroleum hydrocarbons from volatile organic compounds (VOCs) that evaporate during the handling of oil and incomplete fuel combustion that are then deposited into the sea. xxx Does not include urban runoff. x

Introduction and the Oil Spill Problem

Operational

1979x

PART | I

Transportation

1973z

Chapter | 2

19

Spill Occurrences: A World Overview

Tonnes 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 1969

1974

1979

1984

1989

1994

1999

2004

FIGURE 2.2 Annual U.S. offshore oil platform spillage, 1969e2007 (ERC data).

Average platform spillage by decade is shown in Table 2.6. There has been a 30% reduction in annual spillage since 1988e1997 and a 95% reduction since the 1970s. Annual oil spillage from pipelines connected to offshore platforms is shown in Figure 2.3, and by decade in Table 2.7. There has been a 68% reduction in offshore pipeline spillage since 1988e1997. Of the total spillage, 96% is in the Gulf of Mexico. Offshore oil exploration and production spillage was combined to include offshore platforms and pipelines, as well as offshore supply vessels servicing the platforms, as shown in Table 2.8. There has been a 61% reduction in total spillage since 1988e1997 and an 87% reduction since the 1970s.

TABLE 2.6 Average Annual Spillage from U.S. Offshore Oil Platforms (ERC data) Years

Average Annual Spills One Ton or More

Average Annual Tons Spilled

1969e1977

45

3,694

1978e1987

29

192

1988e1997

14

259

1998e2007

20

182

1969e2007

27

1,015

20

PART | I

Introduction and the Oil Spill Problem

Tonnes 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 1969

1974

1979

1984

1989

1994

1999

2004

FIGURE 2.3 Annual oil spillage from U.S. offshore pipelines, 1969e2007 (ERC data).

TABLE 2.7 Average Annual Spillage from U.S. Offshore Oil Pipelines (ERC data) Years

Average Annual Spills One Ton or More

Average Annual Tons Spilled

1969e1977

15

640

1978e1987

10

495

1988e1997

14

1,161

1998e2007

13

373

1969e2007

13

668

Oil spillage per production (i.e., barrels spilled per barrels produced) has decreased over time, as shown in Table 2.9. In other words, despite increases in production, spillage rates have decreased. For every ton of oil produced in the United States, less than 0.000005 tons have spilled from offshore exploration and production activities in the last decade. This is a 71% reduction since the 1988e1997 decade and an 87% reduction since the 1969e1977 decade. While the majority of oil production spills have been recorded in offshore waters, there are reported spills of inland-based oil production wells to inland areas, as shown in Table 2.10. During the oil extraction process at offshore oil platforms, water in the oil reservoir is also pumped to the surface. Industry practice is to treat this

Chapter | 2

21

Spill Occurrences: A World Overview

TABLE 2.8 Average Annual Spillage (tons) from U.S. Offshore Oil Activities (ERC data) Years

Platforms

Pipelines

Offshore Vessels

Total

1969e1977

3,694

640

14

4,348

1978e1987

192

495

39

726

1988e1997

259

1,161

7

1,427

1998e2007

182

373

1

556

1969e2007

1,015

668

15

1,698

TABLE 2.9 U.S. Offshore Oil Exploration/Production Spillage per Production (ERC data) Years

Average Annual Tons Spilled per Tons Produced

1969e1977

0.0000089

1978e1987

0.0000015

1988e1997

0.0000040

1998e2007

0.0000012

1969e2007

0.0000038

TABLE 2.10 Average Annual U.S. Inland Oil Exploration/Production Spillage (ERC data) Years

Average Annual Tons Spilled

1980e1987

521

1988e1997

742

1998e2004

863

1980e2004

705

“produced water” to separate free crude oil, and then to inject the water back into the reservoir, or to discharge the water overboard from the platform. Increasingly, the reinjection process is becoming the preferred technique. The highly diluted oil content in produced water (with a maximum allowable oil

22

PART | I

Introduction and the Oil Spill Problem

TABLE 2.11 Estimated Oil Inputs in Produced Water from U.S. Offshore Oil Exploration/Production

U.S. Region

Produced Water (tons/yr)

Oil/Grease Content (ppm)

Oil/Grease Discharge (tons/yr)

Low

High

“Best”*

Low

High

“Best”

Gulf of Mexico OCS

67,571,429

15

29

20

1,300

2,500

1,700

Louisiana State

26,571,429

15

29

20

450

860

600

e

e

6.6

0

0

5

Texas State

614,286

California Offshore

5,157,143

15

29

18

85

170

85

Alaska State

6,528,571

15

29

15

110

210

110

e

e

20

2,000

3,740

2,500

Total US

106,442,858

*Best estimate as determined by panel of experts in the 2003 NRC study.17

content of 29 ppm) from offshore oil exploration and production processes is generally dispersed very quickly in the open waters where offshore oil platforms are located. The impacts from these inputs in offshore waters have been studied extensively, and, as concluded by the 2003 NRC study, “there is little evidence of significant effects from petroleum around offshore platforms in deep water.”17 The oil inputs from produced water are calculated as shown in Table 2.11don average, 2,500 tons per year, based on the methodology used by the 2003 NRC study based on measurements and assumptions of maximum allowable oil content in produced water (“high”) or lower oil content as reported by offshore operators.17 It is important to note that these inputs are permitted operational discharges that are distinct from accidental spillage previously reviewed. Worldwide estimates on oil spillage and discharges from offshore oil exploration and production activities are shown in Table 2.12. The greatest concern associated with oil pollution from offshore oil and gas exploration is the unlikely event of a catastrophic well “blowout”*. The largest well blowout incidents worldwide are shown in Table 2.13. Fortunately, most blowouts release relatively little oil.29 *

Loss of well control or a blowout is defined as: the uncontrolled flow of formation or other fluids, including flow to an exposed formation (an underground blowout) or at the surface (a surface blowout), flow through a diverter, or uncontrolled flow resulting from a failure of surface equipment or procedures.

Chapter | 2

23

Spill Occurrences: A World Overview

TABLE 2.12 Worldwide Spillage and Discharge from Offshore Oil Exploration and Production Annual Oil Input Estimate (tons)

Study

Estimate Year

Spillage

Operational

Atmospheric

Total

NRC, 1975 [24]

1973

e

e

e

80,000

Kornberg, 1981 [25]

1979

e

e

e

60,000

Baker, 1983 [26]

1981

e

e

e

50,000

NRC, 1985 [27]

1981

e

e

e

50,000

GESAMP, 1993 [28]

1990

e

e

e

47,000

GESAMP, 2007 [13]

1997

3,400

16,350

e

19,750

NRC, 2003 [17]

2000

860

19,000

1,300

21,160

2.3.5. Spills During Oil Transport After extraction from offshore or terrestrial wells, oil is transported by a variety of means to refineries and ultimately to industrial or individual consumersdby tank vessel (tank ships or tankers; tank barges), pipeline, railroad, and tanker truck, each potentially a source of spillage.

2.3.5.1. Spillage from Tank Vessels Tank ships can carry the greatest amount of oildas much as 300,000 tonsdand thus can be the sources of the largest transport-related spills. Tank ships (tankers) carrying crude oil or refined petroleum as cargo spilled an average of 514 tons of oil annually in U.S. waters over the last decade, a 90% reduction since the decade 1988e1997. A breakdown of annual spillage from oil tankers is shown in Figure 2.4. Average annual spillage by decade is shown in Table 2.14. Tank barges carrying oil as cargo spilled an average of 771 tons of oil annually over the last decade, a nearly 67% reduction from the spillage in the decade 1988e1997. Annual spillage volumes are shown in Figure 2.5. A breakdown of average annual spillage from oil tank barges is shown in Table 2.15. Oil transport by tank vessels (tankers and barges) has decreased over the last decades in the United States. Oil spillage from tank vessels in the United States in relation to oil transported by this mode decreased by 71% since the decade 1988e1997 and 81% since the 1980s (Table 2.16). Worldwide estimates of tanker and tank barge spillage made in international studies are shown in Table 2.17.

24

PART | I

Introduction and the Oil Spill Problem

TABLE 2.13 Largest Offshore Exploration and Production Well Blowouts Worldwide (ERC data) Well

Location

Date

Tons

Ixtoc I

Bahia del Campeche, Mexico

June 1979

471,430

Pemex Abkatun 91

Bahia del Campeche, Mexico

October 1986

35,286

Phillips Ekofisk Bravo

North Sea, Norway

April 1977

28,912

Nigerian National Funiwa 5

Forcados, Nigeria

January 1980

28,571

Aramco Hasbah 6

Gulf, off Saudi Arabia

October 1980

15,000

Iran Marine International

Gulf, off Laban Island, Iran

December 1971

14,286

Union Alpha Well 21

Santa Barbara, California, USA

January 1969

14,286

Chevron Main Pass 41-C

Gulf of Mexico, Venice, Louisiana, USA

March 1970

9,286

Pemex Yum II/Zapoteca

Bahia del Campeche, Mexico

October 1987

8,378

Shell South Timabalier B-26

Gulf of Mexico, Bay Marchand, Louisiana, USA

December 1970

7,585

2.3.5.2. Spills from Pipelines In inland areas, underground and above-ground pipelines transport large quantities of crude oil and refined fuels, particularly diesel, gasoline, heavy fuel oil, and trans-mix.* Spillage from pipelines in coastal and inland areas is shown in Table 2.18 and Figure 2.6. During the last decade, coastal and inland pipelines spilled an average of 11,000 tons of oil annually. This represents a 35% reduction in spillage since 1988e1997 and 70% since the 1970s. In these analyses, coastal and inland pipelines were considered to encompass all parts of the pipeline system, including gathering pipes, transmission pipes, breakout tanks, pump stations, and tank farms directly associated with and operated by pipeline companies. Offshore pipelines were considered separately under *

Usually a combination of No. 2 fuel oil (diesel) and No. 6 heavy fuel oil.

Chapter | 2

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Spill Occurrences: A World Overview

Tonnes 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0

1962

1967

1972

1977

1982

1987

1992

1997

2002

2007

FIGURE 2.4 Spills into U.S. waters from tank ships, 1962e2007 (ERC data).

TABLE 2.14 Average Annual Oil Spillage from Tank Ships in U.S. Waters (ERC data) Years

Average Number of Spills One Ton or More

Average Annual Tons Spilled

1962e1967

e

7,162

1968e1977

301

27,513

1978e1987

153

8,607

1988e1997

55

6,028

1998e2007

19

514

offshore exploration and production. It should be noted that a significant portion of oil (about 85%) that spills from inland pipelines goes to containment areas around breakout tanks or to solid ground rather than directly into surface waters. With concerns about the aging pipeline infrastructure and vulnerability of pipelines for spillage, there have been a number of regulatory changes for pipelinesdthe Oil Pollution Act of 1990 (OPA 90), the 2002 Pipeline Safety Act (PSA), and the 2006 Pipeline Integrity, Protection, Enforcement, and Safety (PIPES) Act, which have improved pipeline safety and reduced spillage. Pipeline spillage amounts by oil type and per unit of oil transport are shown in Table 2.19. Spillage per unit transport has decreased 37% since the decade 1988e1997, and 57% since the 1980s.

26

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Introduction and the Oil Spill Problem

Tonnes 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0

1968

1973

1978

1983

1988

1993

1998

2003

FIGURE 2.5 Spills into U.S. waters from tank barges, 1968e2007 (ERC data).

TABLE 2.15 Average Annual Oil Spillage from Tank Barges in U.S. Waters (ERC data) Years

Average Number of Spills One Ton or More

Average Annual Tons Spilled

1968e1977

368

4,547

1978e1987

290

7,570

1988e1997

123

3,269

1998e2007

54

776

1968e2007

186

4,040

2.3.5.3. Spills from Railroads Railroads spilled 200 tons of oil annually as cargo in tankcars and as fuel. This is a 34% reduction since the decade 1988e1997. Average annual railroad spillage and spillage by ton-miles transported are shown in Table 2.20. (A tonmile is a measure of the transport of oil one ton the distance of one mile.) The spillage rate has decreased in the last three decades. Spills from railroads often go to ballast and do not always directly impact waterways. 2.3.5.4. Spillage from Tanker Trucks Tanker trucks carrying oil (usually fuels) as cargo spilled an average of 1,300 tons of oil annually in the last decade, a 76% increase since the decade 1988e1997.

Chapter | 2

27

Spill Occurrences: A World Overview

TABLE 2.16 Oil Spillage by Tank Vessels in Relation to Oil Transported in U.S. Waters (ERC data) Average Annual Spillage (tons) Tankers

Tank Barges

Combined

Average Annual Spillage per Billion Ton-Miles* Oil Transport

1978e1987

8,607

7,570

16,177

27.40

1988e1997

6,028

3,269

9,297

18.22

1998e2007

514

776

1,290

5.28

Time Period

*Ton-miles combine volume and distance of transport.

TABLE 2.17 Estimates of Worldwide Annual Tank Vessel Spillage Estimate of Average Annual Tank Vessel (Tank Ship and Tank Barge) Spillage (tons) Study

1970s

1980s

1990s

NRC, 1975

300,000

e

e

Kornberg, 1981

300,000

e

e

Baker, 1983

e

390,000

e

NRC, 1985

e

400,000

e

GESAMP, 1993

e

564,000*

e

GESAMP, 2007

e

e

157,900

NRC, 2003

e

e

100,000

Etkin, 2001

372,878

98,866

184,460y

ERC Data

431,381

213,991

136,991z

*Includes operational discharges from vessels. y Includes 1991 Gulf War-related tanker spillage. z Excludes 1991 Gulf War-related tanker spillage.

This may be attributed to better reporting of these incidents to local authorities that usually handle these incident responses. Spills from tanker trucks often go to pavements and do not directly impact waterways. Average annual spillage is in Table 2.21. There are no reliable international data on this source type.

28

PART | I

Introduction and the Oil Spill Problem

TABLE 2.18 Oil Spillage from U.S. Inland and Coastal Pipeline Systems (ERC data) Years

Average Annual Number of Spills One Ton or More

Average Annual Tons

1968e1977

276

37,049

1978e1987

172

25,885

1988e1997

140

16,900

1998e2007

195

10,965

1968e2007

196

22,828

Tonnes 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0

1968

1974

1979

1984

1989

1994

1999

2004

FIGURE 2.6 Spills into U.S. waters from pipelines, 1968e2007 (ERC data).

2.3.6. Spillage from Oil Refining Each year, on average, over 8.6 billion tons of imported and domesticallyproduced crude oil are refined into hundreds of petroleum-based products and fuels at the 162 refineries in the United States. Spillage from oil refineries averaged 1,700 barrels annually over the last decade, about a 19% reduction since the decade 1988e1997 and a 27% reduction in spillage per barrel of oil throughput at refineries. Average annual spillage is shown in Table 2.22. The lower spillage for the 1980e1987 time period is likely a data artifact since spill sources in reports were not always accurately identified (e.g.,

Chapter | 2

29

Spill Occurrences: A World Overview

TABLE 2.19 Average Annual U.S. Oil Pipeline Spillage by Oil Type and Transport (ERC data) Tons Spilled per Billion Ton-Miles* Transport

Spillage (tons) Years

Crude

Refined

Total

Crude

Refined

Total

1980e1987

11,314

6,157

17,471

35.24

25.06

30.71

1988e1997

16,384

9,292

25,676

48.99

40.42

45.50

1998e2007

10,855

6,558

17,413

32.38

25.95

29.71

1980e2007

7,716

3,249

10,965

27.11

11.89

19.88

*A ton-mile is one ton of oil being transported one mile.

TABLE 2.20 Average Annual Estimated U.S. Oil Spillage from Railroads (ERC data) Years

Tons Spilled per Year

Tons Spilled per Billion Ton-Miles Transport

1980e1987

332

26.7

1988e1997

309

18.6

1998e2007

204

10.3

1980e2007

278

16.2

TABLE 2.21 Average Annual Estimated U.S. Oil Spillage from Tanker Trucks (ERC data) Years

Tons per Year

Tons Spilled per Billion Ton-Miles Transport

1980e1987

698

25.5

1988e1997

745

26.2

1998e2007

1,312

41.2

1980e2007

934

31.6

30

PART | I

Introduction and the Oil Spill Problem

TABLE 2.22 Average Annual Oil Spillage from U.S. Refineries (ERC data)

Years

Tons Spilled

Annual Refining Capacity (tons)

Refinery Utilization

Annual Throughput (tons)

Spillage per Refinery Throughput

1980e1987

502

9.17 109

78.95%

0.66 109

0.00000076

1988e1997

2,145

9.41 109

89.71%

0.72 109

0.00000296

91.62%

0.80 10

9

0.00000216

87.32%

0.73 10

9

0.00000208

1998e2007 1980e2007

1,734

10.33 10

1,529

9.66 10

9

9

a refinery may merely have been identified as a “facility”). There were also changes in reporting requirements in 1986 that authorized the Toxics Release Inventory (TRI) to track facility releases of a variety of chemicals and toxic substances. While crude oil and refined petroleum products themselves are not encompassed by the TRI-reporting requirements, some of their additives and chemical components are listed. Overall, this created a greater awareness of the need to report discharges from refineries. During refining, wastewater containing minute concentrations of oil is legally discharged in effluents, as permitted under the National Pollutant Discharge Elimination System (NPDES). The NPDES-permitted refinery effluents contain no more than five parts of oil per million parts of wastewater. The effluents are generally discharged in rivers and coastal areas where the already dilute oil concentrations are quickly diluted even further. The environmental impacts of refinery effluents have been studied fairly extensively. Environmental impacts from the oil in the effluents are extremely low and localized. Refineries are, however, generally located in industrial areas that have other permitted discharges, making it difficult to separate the effects of oil in effluents from those of background concentrations of other contaminants from other point and nonpoint sources. A comprehensive review of the ecological impacts of refinery effluents concluded that any minor impacts are limited to the areas close to the outfalls, but that it is difficult to distinguish these impacts from other pollution sources.30 The total amount of aqueous effluent discharged from oil refineries has decreased by 20% over the last 40 years due to increases in the use of air cooling and recirculation of cooling water. In addition, the toxicity of effluent discharges has decreased significantly owing to the implementation of various wastewater treatment systems.30,31 The estimated maximum discharge of oil in refinery effluents over the last decade is 7,700 tons per year. This estimate is the equivalent of less than 0.00001 ton of oil for each ton of oil processed, and is based on the following assumptions: wastewater production as a function of refinery capacity average for the last decade is 2.37

Chapter | 2

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Spill Occurrences: A World Overview

TABLE 2.23 Estimated Annual Oil Discharged in U.S. Oil Refinery Effluents (ERC data)

Years

Throughput (billion tons)

Wastewater Tons per Ton Throughput

Wastewater (billion bbl)

Oil in Effluent (tons)

1985e2007

0.66

2.15

1.17

5,837

1985e1987

0.72

1.69

1.49

7,465

1988e1997

0.80

2.05

1.91

9,538

1998e2007

0.73

2.38

1.55

7,740

barrels of wastewater (refinery effluent) produced per barrel of refining capacity; and effluents contain 5 ppm of oil (based on NPDES guidelines). There are a number of estimates of the amount of wastewater produced per unit of refining capacity. The average of the two best-documented sources was taken.31,32 Average annual refinery effluent discharges in the United States are shown in Table 2.23. The average annual oil in legally-permitted refinery effluent discharges is based on an assumption of maximum effluent oil concentration of 5 ppm. This value is the maximum allowed. Actual oil concentrations in effluents are likely to be lower. Estimates of international coastal refinery spillage and effluent oil content were made as shown in Table 2.24. Estimates in the 2007 GESAMP study were made using the same methodology as for U.S. refinery effluents, with the exception that the oil content in the effluent was assumed to vary between 5 ppm and 25 ppm, depending on national laws and practices.13

TABLE 2.24 Estimates of Worldwide Oil Refinery Spillage and Effluent Discharges Estimated Average Annual Inputs to Marine Waters (tons)

Study

Year(s)

Refinery Spillage

Oil in Refinery Effluent

Total Refinery Inputs

NRC, 1975

1970s

200,000

e

200,000

Baker, 1983

1980s

100,000

e

100,000

GESAMP, 2007

1990s

e

179,547

179,547

32

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Introduction and the Oil Spill Problem

2.3.7. Spillage Related to Oil Consumption and Usage Refined petroleum products are used in a wide variety of applications, including fuels for transportation, heating, manufacturing, and electricity production. Spillage of oil from sources that “consume” or use oil is generally outside of the realm of the petroleum industry itself, but is presented here for perspective on total oil inputs.

2.3.7.1. Spillage from Nontank Vessels “Nontank vessels” (e.g., cargo ships) carrying oil as bunker fuel and for operations spill an average of 230 tons of oil annually, a 43% reduction since the decade 1988e1997. Average annual spillage from these vessels is shown in Table 2.25. At the same time, the shipment of dry cargo (i.e., nonpetroleum shipments) by vessels has increased by 43% over the last 30 years in the United States (U.S. Army Corps of Engineers). The rate of spillage from these cargo ships in relation to the tonnage of cargo moved in U.S. waters during that time period is shown in Table 2.26. Spillage per cargo shipment has declined by 50% since the decade 1988e1997. Worldwide nontank vessel spillage was estimated in several international studies (Table 2.27). Spillage from smaller vessels (e.g., passenger, fishing, recreational, and unclassified vessels) averaged under 600 tons annually over the last decade in the United States, a nearly 34% reduction since the decade 1988e1997. Average annual spillage from these vessels is shown in Table 2.28. There are no reliable worldwide estimates of spillage from smaller vessels. Operational discharges of lubricant oils from the vessels within ports, during transit within ports, and while moored at docks contribute a significant amount of oil to U.S. watersdabout 2,800 tons annually.33 Leakages occur from stern tubes and other submerged machinery, as well as from on-deck

TABLE 2.25 Average Annual Oil Spillage from Nontank Vessels in U.S. Waters (ERC data) Years

Annual Number of Spills (1 ton or more)

Annual Tons Spilled

1973e1977

100

149

1978e1987

85

969

1988e1997

83

402

1998e2007

37

229

1973e2007

73

479

Chapter | 2

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Spill Occurrences: A World Overview

TABLE 2.26 Cargo Vessel Oil Spillage per Dry Cargo Shipments in U.S. Waters (ERC data) Years

Dry Cargo Shipment (million short tons)

Annual Tons Spilled

Tons Spilled per Million Short Tons Shipped

1978e1987

1,057

969

0.90

1988e1997

1,256

402

0.32

1998e2007

1,382

229

0.16

1978e2007

1,232

534

0.46

TABLE 2.27 Estimates of Worldwide Nontank Vessel Spillage Estimate of Average Annual Nontank Vessel Spillage (tons) Study

1970s

1980s

1990s

NRC, 1975

750,000*

e

e

Kornberg, 1981

200,000*

e

e

Baker, 1983

e

340,000*

e

NRC, 1985

e

320,000*

e

GESAMP, 2007

e

NRC, 2003

e

533,000* 7,100

Etkin, 2001

1,000

4,024

5,454

ERC Data

2,735

23,811

10,248

*Includes operational discharges from nontank vessels.

machinery. Based on a study by Etkin, these inputs are estimated to be as shown in Table 2.29, as calculated for the past five years.33 Previous time periods were adjusted based on the overall amount of shipping in U.S. waters. Operational inputs of oil and gasoline from two-stroke engines in the United States were estimated by the 2003 NRC study17 to average 7,000 tons annually and by the 2007 GESAMP study13 to average 53,000 tons annually worldwide (Table 2.30). It should be noted that this estimate has been questioned by a number of researchers (personal communications) with regard to the assumption that all of the gasoline enters the water rather than combusts or evaporates. The use of two-stroke engines of the type mentioned in this study has significantly decreased in the last few years.

34

PART | I

Introduction and the Oil Spill Problem

TABLE 2.28 Estimated Oil Spillage from Smaller Vessels in U.S. Waters (ERC data) Years

Annual Tons Spilled

1973e1977

2,123

1978e1987

939

1988e1997

900

1998e2007

595

1973e2007

999

TABLE 2.29 Vessel Operational Lubricant Leakage in U.S. and Worldwide Ports33 Lubricant Discharges (average annual tons) Stern Tube

Other Operational

Total

Years

U.S.

Worldwide

U.S.

Worldwide

U.S.

Worldwide

1980e1987

1,064

19,558

1,036

22,082

2,101

41,488

1988e1997

1,246

22,903

1,213

25,859

2,460

48,584

1998e2007

1,400

25,734

1,363

29,055

2,764

54,589

1980e2007

1,237

22,738

1,204

25,672

2,442

48,233

2.3.7.2. Spillage from Facilities Coastal facilities (other than refineries) spill an estimated 600 tons of oil in the United States annually, a 72% reduction from the decade 1988e1997. Average annual facility spillage in the United States and worldwide is presented in Table 2.31. It is important to note that spillage volumes from coastal facilities often include oil that spills into secondary containment. A secondary containment system provides an essential line of defense in preventing oil from spreading and reaching waterways in the event of the failure of an oil container (e.g., a storage tank) or the primary containment. The system provides temporary containment of the spilled oil until a response can be mounted. In the last decade, gas stations and truck stops spilled an average of 100 tons of oil annually, a nearly 48% decrease since the decade 1988e1997. This

Chapter | 2

Spill Occurrences: A World Overview

35

TABLE 2.30 Estimates of Oil Inputs from Two-Stroke Recreational Vessels in the United States and Worldwide Estimated Average Annual Tons Input

Region U.S. Atlantic*

3,100

U.S. Gulf of Mexico*

1,540

U.S. Pacific and Alaska*

2,306

U.S. Total*

6,946

Worldwide*

53,000

*Estimates based on analyses in the 2003 NRC study.17

TABLE 2.31 Estimated Oil Spillage from Coastal Marine Facilities in U.S. and Worldwide Waters Years

U.S. Annual Tons Spilledy

Worldwide Annual Tons Spilled

1973e1977

8,889

150,000z

1978e1987

6,112

50,000x

1988e1997

2,151

1998e2007

604

1973e2007

3,803

e 2,400*e12,000** e 13

*From 2007 GESAMP study based on methods in the 2003 NRC study.17 The 2007 GESAMP study13 had a second estimate of 3.9 106 tons annually. y ERC data z [24] x [25, 26] **[23]

includes all spillages that occur at gas station facilities and truck stops, including spills that occur during the transfer of fuels from tanker trucks. Average annual spillage from these sources is shown in Table 2.32. Spills at gas stations and truck stops often go to pavements and other substrates, reducing the direct impacts to waterways. There are no reliable estimates of worldwide spillage rates. Inland facilities regulated under the United States’ Environmental Protection Agency’s Spill Prevention, Control, and Countermeasures (SPCC) program other than refineries and production wells, covered separately in these analyses,

36

PART | I

Introduction and the Oil Spill Problem

TABLE 2.32 Estimated U.S. Oil Spillage from Gas Stations and Truck Stops (ERC data)* Year

Annual Spillage (tons)

1980e1987

171

1988e1997

223

1998e2007

116

1980e2007

170

*Based on reported data reported to the relevant state and local authorities in the 50 U.S. states, as well as data reported to federal authorities. These data do not include leaking underground storage tanks that leak over long periods of time. These data are tracked separately and are not considered emergency spill incidents. Since gas stations are regulated by the EPA, facilities spillage at these facilities of at least 50 gallons (0.17 tons) that occur during these facilities are included. Smaller spills (less than 50 gallons) are not included.

TABLE 2.33 Estimated U.S. Oil Spillage from Inland EPA-Regulated Facilities (ERC data) Years

Annual Spillage (tons)

1980e1987

4,963

1988e1997

35,002

1998e2007

8,525

1980e2007

16,963

spill an average of 8,500 tons of oil annually, a 76% reduction since the decade 1988e1997 (Table 2.33). Spills at inland facilities often go to pavements and other substrates, including secondary containments, reducing direct impacts to waterways. There are no reliable estimates of worldwide spillage rates. Oil spillage from home-heating oil tanks, which are not regulated by the EPA unless the tanks are in sizes larger than 34 tons, amounts to 70 tons of oil annually, a slight decrease from the decade 1988e1997 (Table 2.34). Note that this does not include slow leakages from underground storage tanks. There are no reliable estimates of worldwide spillage rates from residential tanks. Motor vehicles that carry oil as fuel rather than cargo spill about 285 tons of oil annually in the United States, double that for the decade 1988e1997 (Table 2.35). The spillage is associated with greater motor vehicle traffic, as well as

Chapter | 2

Spill Occurrences: A World Overview

37

TABLE 2.34 Estimated Oil Spillage from U.S. Residential Heating Oil Tanks (ERC data) Years

Annual Tons Spilled

1980e1987

26

1988e1997

74

1998e2007

71

1980e2007

59

TABLE 2.35 Estimated U.S. Oil Spillage from Motor Vehicles (excluding tanker trucks) (ERC data) Year

Annual Tons Spilled

Average 1980e1987

39

Average 1988e1997

170

Average 1998e2007

295

Average 1980e2007

168

better reporting by local authorities that are often the emergency responders. Motor vehicle spills* often go to pavements and do not directly impact waterways. Since the data only include spills of less than 1 ton, most passenger vehicles are excluded. There are no reliable estimates of worldwide spillage from motor vehicles.

2.3.7.3. Spillage from Aircraft and Other Sources Aircraft spill an estimated 50 tons of jet fuel annually to inland areas. These spills generally occur at airports during fueling, or occasionally from an accident. Aircraft spill an additional 530 tons annually to U.S. marine waters, based on a 2003 NRC study.17 These spills occur from two sources: through the deliberate discharge or jettisoning of jet fuel due to emergency conditions aboard an aircraft, or through the release of partially burned fuel in inefficient engines or operating modes.17,34 This type of spillage also occurs over inland areas, but there are no *

Note that tanker trucks carrying oil as cargo are considered separately.

38

PART | I

Introduction and the Oil Spill Problem

TABLE 2.36 Estimated U.S. Oil Spillage (bbl) from Other Inland Sources (ERC data) Years

Inland Aircraft (annual tons)

Inland Unknown (annual tons)

1980e1987

2

138

1988e1997

23

314

1998e2007

49

74

1980e2007

26

190

current estimates of these inputs. Total aircraft input in the United States is estimated to be about 580 tons of oil annually. The 2003 NRC study estimated worldwide marine inputs from jettisoned aircraft fuel to be about 7,500 tons annually.17 Average annual spillage from aircraft and miscellaneous unknown (unidentified) inland sources in the United States is shown in Table 2.36.

2.3.7.4. Oil Inputs from Urban Runoff About 50,000 tons of oil enters U.S. marine waters each year through urban runoff, based on a 2003 NRC study.17 Urban runoff is the accumulation of drops of oil that leak from automobiles, trucks, and other vehicles, as well as small chronic spillages that occur from other land-based sources. Oil washes off into storm sewers, culverts, and other waterways into streams and rivers that enter marine waters. Because the exact spillage source cannot be pinpointed, it is termed “nonpoint source” pollution. The U.S. inputs are broken down by region in Table 2.37. Studies that included worldwide estimates of oil in urban runoff are shown in Table 2.38.

TABLE 2.37 Estimates of U.S. Oil Inputs from Urban Runoff Region

Estimated Average Annual Tons of Oil Input*

Atlantic

31,500

Gulf of Mexico

12,600

Pacific

5,829

Alaska

80

Total

50,009

*Estimates based on analyses in the NRC 2003 study.17

Chapter | 2

39

Spill Occurrences: A World Overview

TABLE 2.38 Estimates of Worldwide Marine Oil Inputs from Urban Runoff Estimate of Average Annual Oil Inputs from Urban Runoff (tons) Study

1970s

1980s

1990s

NRC, 1975

2,500,000

e

e

Kornberg, 1981

2,100,000

e

e

Baker, 1983

e

1,430,000

e

NRC, 1985

e

1,080,000

e

NRC, 2003

e

e

140,000

2.3.8. Oil Inputs from Potentially Polluting Sunken Shipwrecks Potential future and documented current oil leakage and discharges from sunken ships in marine waters is an issue of concern worldwide. A study conducted in 1977 drew attention to the oil discharges from a large number of oil tankers sunk during military operations in World War II along the U.S. western, eastern, and southern (Gulf of Mexico) coasts.18 While the tankers had been sunk over 30 years earlier, oil was still periodically leaking from the vessels, which were acting as “seeps.” Many of the tankers were still relatively intact, though their structural integrity was uncertain. The issue of oil pollution from sunken World War II tankers and military vessels was further brought to public attention after several incidents of oil leaking from several vessels (notably the S.S. Jacob Luckenbach off the Pacific coast of the United States, the USS Mississinewa in Micronesia, and the German warship Blu¨cher off Oslo, Norway) in the late 1990s to 2004. These sunken vessels were identified as the sources of “mystery spills” and discharges that impacted shorelines and other resources.35 The South Pacific Environment Programme (SPREP) has conducted surveys of wrecks in the South Pacific region particularly impacted by World War II military vessel sinkings.36,37 In 2005, the American Petroleum Institute and the sponsors of the International Oil Spill Conference* commissioned a study, Potentially Polluting Wrecks in Marine Waters, which involved developing a databasey of recorded *

International Maritime Organization, U.S. Coast Guard, U.S. Environmental Protection Agency, International Petroleum Industry Environmental Conservation Association, Minerals Management Service, and National Oceanic and Atmospheric Administration.

y

The proprietary database was developed by ERC.

40

PART | I

Introduction and the Oil Spill Problem

FIGURE 2.7 Approximate location of potentially polluting shipwrecks* (ERC data).

vessel sinkings for tankships of at least 150 gross registered tons (GRTs) carrying oil and nontank vessels of at least 400 GRTs that carried oil as fuel/ bunkers (and for operations); an analysis of the distribution of and likely amount of oil contained in these vessels; and an examination of the environmental, regulatory, political, technical, and financial issues associated with these sources of petroleum.38 The data analysis revealed that there were at least 8,569 recorded vessel sinkings worldwide, of which 1,583 were tankships and 6,986 were nontank vessels. An estimated 2.5 to 20.4 million tons of oil is thought to be present in these shipwrecks. The shipwrecks are distributed throughout the world, as shown in Figure 2.7. The data in the 2005 Michel et al. study were analyzed regionally, as summarized in Table 2.39.38 This oil will not necessarily discharge, but there is the potential that it will, with the actual probability of discharge depending on vessel integrity and condition, age, depth at which the wrecks rest, temperature of the waters, and type of oil. Heavier fuels at greater depths may be nearly solid, and many of the vessels may be largely intact. On the other hand, the greatest potential for spillage is with the older vessels, particularly those from World War II, which were often built according to lower standards than more modern vessels. The potential for impacts depends largely on the location of the wrecks. Those in nearshore waters tend to present the greatest potential for impacting *

Dots indicate approximate locations based on Marsden square (10-degree latitude/longitude). Because many of the vessels are “war-graves” and there are also safety concerns, authorities aim to prevent plundering or diving exploration. The exact locations of many vessels are uncertain or are classified or confidential.

Chapter | 2

Spill Occurrences: A World Overview

41

TABLE 2.39 Worldwide Potential Pollution from Sunken Tankers and Nontank Vessels (ERC data) Estimated Oil Content of Shipwrecks (tons) Region

Minimum

Maximum

North Atlantic Ocean

951,000

7.5 million

South Atlantic Ocean

165,000

0.5 million

North Pacific Ocean

221,000

1.7 million

South Pacific Ocean

521,000

4.2 million

Indian Ocean

264,000

2.2 million

shorelines. The impacts of discharges from these vessels in the open ocean are likely to be less severe than those closer to shore because of the natural dispersion that would break the oil into smaller concentrations. Much of the oil involved is likely to be heavier and would most likely form tar balls rather than larger slicks unless released in a large mass. The experts who conducted the 2005 Michel et al. study concluded that most of the vessels were likely to release oil in small quantities over a longer period of time or had already started to do so, acting almost as a “natural seep.” Nevertheless, there is the potential for a vessel to suddenly release a much larger quantity of oil if a radical change takes place in the vessel’s structural integrity.38 The political, regulatory, and financial issues associated with these shipwrecks are extremely complex due to jurisdictional concerns. Removing the oil and other hazardous materials, as well as munitions, from these vessels involves complex, dangerous, and expensive salvage operations. It is unclear who would finance or regulate these operations, especially for the large number of World War II vessels involved. Because of the complex issues presented by these wrecks and the overwhelming number of potentially polluting wrecks, an approach involving scientifically based risk assessments and cost-benefit analyses has been promoted by several organizations, government agencies, and researchers to prioritize those wrecks that poset the highest environmental risk for oil and hazardous material removal operations.38-44

2.3.9. Summary of Oil Spillage Estimates of average annual U.S. oil spillage by decade from all source categories are summarized in Table 2.40. Over the last decade, the largest source category of spillage is inland pipelines, followed by EPA-regulated facilities. The oil spillage reported here does not reflect the amounts of oil

42

PART | I

Introduction and the Oil Spill Problem

TABLE 2.40 Estimated Total Average Annual U.S. Oil Spillage (tons) 1969e 1977

1978e 1987

1988e 1997

1998e 2007

Production

4,491

1,243

2,169

1,420

5.07%

Offshore Platform Spills

3,694

192

259

182

0.65%

640

495

1,161

373

1.33%

Offshore Supply Vessels

14

35

7

1

0.00%

Inland Production Wells

143

521

742

863

3.08%

Refining

429

502

2,145

1,734

6.19%

Refinery Spills

429

502

2,145

1,734

6.19%

Transport

69,809

43,092

27,250

13,770

49.16%

Inland Pipelines

37,049

25,885

16,900

10,965

39.15%

Tanker Trucks

429

698

745

1,312

4.68%

Railroads

286

332

309

204

0.73%

Tank Ships

27,499

8,607

6,028

514

1.83%

Tank Barges

4,547

7,570

3,269

776

2.77%

16,932

13,887

39,789

11,088

39.58%

714

969

402

229

0.82%

2,123

939

900

595

2.12%

171

171

223

116

0.41%

21

26

74

71

0.25%

4,286

4,963

35,002

8,525

30.43%

529

531

552

578

2.06%

Coastal Facilities

8,889

6,112

2,151

604

2.16%

Inland Unknown

129

138

314

74

0.26%

71

39

170

295

1.05%

91,660

58,723

71,354

28,011

100.00%

Source Type

Offshore Pipelines

Storage and Consumption Nontank Vessels Other Vessels Gas Stations and Truck Stops Residential Inland EPA-Reg Facilities* Aircrafty

Motor Vehicles Total

% Total 1998e2007

*Excludes refineries, gas stations, and production wells. y Includes aircraft in inland areas plus estimates of marine inputs (based on NRC, 2003).

Chapter | 2

43

Spill Occurrences: A World Overview

that were contained or recovered. It also does not reflect the differences between oil that is spilled directly into marine or freshwater systems and oil that is spilled onto other surfaces, including containment areas around storage tanks in tank farms. The properties of the oil spilled (crude vs. refined, heavy vs. light) and the locations in which the oil spills (marine waters, inland waters, dry surfaces, wetlands, industrial zones) will largely determine the impacts of these spills and should be considered in addition to the actual amounts of oil spilled. Total U.S. oil inputs to marine and inland waters, including spills, runoff, and all operational discharges are shown in Table 2.41. TABLE 2.41 Estimated Total Average Annual U.S. Oil Inputs (tons) Source Type

1969e 1977

1978e 1987

1988e 1997

1998e 2007

% Total 1998e2007

Production

5,876

2,431

3,438

2,930

2.15%

Offshore Platform Spills

3,694

192

259

182

0.13%

Offshore Pipeline Spills

640

495

1,161

373

0.27%

Offshore Supply Vessel Spills

14

39

7

1

0.00%

Inland Production Well Spills

521

742

863

705

0.52%

1,007

963

1,148

1,669

1.23%

35,963

52,758

68,910

55,915

41.09%

429

502

2,145

1,734

1.27%

Refinery Effluents

35,534

52,256

66,765

54,181

39.82%

Transport

69,882

43,084

27,163

13,864

10.19%

Inland Pipelines

37,049

25,885

16,900

10,965

8.06%

Tanker Trucks

429

698

745

1,312

0.96%

Railroads

332

309

204

278

0.20%

Tank Ships

27,513

8,607

6,028

514

0.38%

Tank Barges

4,547

7,570

3,269

776

0.57%

12

15

17

19

0.01%

67,841

65,497

91,595

63,357

46.56%

Produced Water Refining Refinery Spills

Tank Vessel Operational Discharge Storage and Consumption

(Continued )

44

PART | I

Introduction and the Oil Spill Problem

TABLE 2.41 Estimated Total Average Annual U.S. Oil Inputs (tons)dcont’d Source Type Nontank Vessels

1969e 1977

1978e 1987

1988e 1997

1998e 2007

% Total 1998e2007

149

969

402

229

0.17%

Other Vessels

2,123

939

900

595

0.44%

Vessel Operational Discharge

2,000

2,086

2,443

2,745

2.02%

171

223

116

170

0.12%

21

26

74

71

0.05%

4,286

4,963

35,002

8,525

6.27%

2

2

23

49

0.04%

Coastal Facilities (Nonrefining)

8,889

6,112

2,151

604

0.44%

Inland Unknown

129

138

314

74

0.05%

71

39

170

295

0.22%

50,000

50,000

50,000

50,000

36.75%

179,562

163,770

191,106

136,066

100.00%

Gas Stations and Truck Stops Residential Inland EPA-Regulated Facilities** Aircrafty

Motor Vehicles Urban Runoff Total

**Excludes refineries, gas stations, and production wells. y Includes aircraft in inland areas, plus estimates of marine inputs based on the 2003 NRC study [17].

Although annual spill amounts vary from year to year, often due to one or two particularly large incidents, there has been a general downward trend in U.S. spills in the past decade, and an even greater downward trend since 1989, the year of the Exxon Valdez spill (Figure 2.8). Worldwide trends are shown in Figure 2.9. The large spill in 1979 from the Ixtoc I well blowout dominates the spillage. War-related intentional spillage, such as that in the 1991 Gulf War, has not been included. Despite general downward trends in spills in the United States and worldwide, it is important that spill response preparedness be maintained due to the continuing risk of spills, including worst-case discharge scenarios. Most spills will continue to be “routine” in that they are relatively small and easily responded to with local resources. At the same time, occasional large spills, along with increasing public expectations for effective spill response and increased spiller liability, have necessitated complex contingency planning for

Chapter | 2

45

Spill Occurrences: A World Overview

Tonnes 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0

1968

1973

1978

1983

1988

1993

1998

2003

FIGURE 2.8 Annual oil spillage into U.S. waters with reduction trends (ERC data).

Tonnes 2,000,000 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0

1970

1975

1980

1985

1990

1995

2000

FIGURE 2.9 Worldwide oil spillage with reduction trends (ERC data).

increasingly rare high-impact events.22 For example, the United States with its experience in the 1989 tanker Exxon Valdez, which involved the spillage of over 37,000 tons of oil, has not experienced a worst-case discharge scenario, defined as the complete release of the contents of a fully loaded oil tanker or large storage facility. Had the Exxon Valdez released its entire contents, about five times as much oil would have spilled into Prince William Sound. The complexity of the spill response and the impact of the spill is difficult to envision, but must be planned for. The magnitude of the spill from the MC-252

46

PART | I

Introduction and the Oil Spill Problem

well (otherwise referred to as Deepwater Horizon spill) that occurred during April through July 2010 has not yet been verified, though it has been been confirmed to be the largest spill in US history.

REFERENCES 1. Lees GM, et al. The Eastern Hemisphere. In: Pratt WE, Good D, editors. World Geography of Petroleum, 159. Princeton University Press; 1950. 2. Levorson AI. Geology of Petroleum, 14. San Francisco, CA: Freeman Press; 1954. 3. Hodgson SF. Onshore Oil and Gas Seeps in California, California Division of Oil and Gas. Department of Conservation; 1987. 4. Allen A, Schlueter RS, Mikolaj PG. Natural Oil Seepage at Coal Oil Point, Santa Barbara, California. Science 1970;974. 5. Hornafius JS, Quigley D, Luyendyk BP. The World’s Most Spectacular Marine Hydrocarbon Seeps (Coal Point, Santa Barbara Channel, California): Quantification of Emissions. J. Geophys. Res 1999;703. 6. Kvenvolden KA, Simoneit BRT. Hydrothermically Derived Petroleum: Examples from Guaymas Basin, Gulf of California, and Escanaba Trough, Northeast Pacific Ocean. Amer. Assoc. Petrol. Geolog. Bull. 1990;223. 7. Leifer I, Luyendyk B, Broderick K. Tracking Seep Oil from Seabed to Sea Surface and beyond at Coal Oil Point, California, Proceedings of the American Association of Petroleum Geologists (AAPG). Salt Lake City: Utah; 2003. 8. Chernova TG, Rao PS, Pikovskii Y, Alekseeva TA, Nagender NB, Ramalingeswara RB, et al. The Composition and the Source of Hydrocarbons in Sediments Taken from the Tectonically Active Andaman Backarc Basin, Indian Ocean. Mar. Chem. 2001;1. 9. Gupta RS, Qasim SZ, Fondekar SP, Topgi RS. Dissolved Petroleum Hydrocarbons in Some Regions of the Northern Indian Ocean. Mar. Pollut. Bull. 1980;65. 10. Venkatesan MI, Ruth E, Rao PS, Nath BN, Rao BR. Hydrothermal Petroleum in the Sediments of the Andaman Backarc Basin, Indian Ocean. Appl. Geochem. 2003;845. 11. MacDonald IR. Natural Oil Spills. Scientific American 1998;57 (Nov). 12. Wilson RD, Monaghan PH, Osanik A, Price LC, Rogers MA. Natural Marine Oil Seepage. Science 1974;857. 13. GESAMP (IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection). Estimates of Oil Entering the Marine Environment from Sea-Based Activities. GESAMP Reports and Studies 2007;75. 14. Kvenvolden KA, Cooper CK. Natural Seepage of Crude Oil into the Marine Environment. Geo-Marine Letters 2003;140. 15. Kvenvolden KA, Harbaugh JW. Reassessment of the Rates at Which Oil from Natural Sources Enters the Marine Environment. Mar. Environ. Res. 1983;223. 16. Quigley DC, Hornafius JS, Luyendyk BP, Francis RD, Clark J, Washburn L. Decrease in Natural Marine Hydrocarbon Seepage near Coal Oil Point, California, Associated with Offshore Oil Production. Geology 1999;1:047. 17. National Research Council Committee on Oil in the Sea. Oil in the Sea III: Inputs, Fates, and Effects, National Research Council Ocean Studies Board and Marine Board Divisions of Earth and Life Studies and Transportation Research Board. Washington, DC: National Academy Press; 2003.

Chapter | 2

Spill Occurrences: A World Overview

47

18. Campbell B, Kern E, Horn D. Impact of Oil Spillage from World War II Tanker Sinkings, Report No. MITSG 77e4 Index No. 77-304-Nnt. Cambridge: Massachusetts Institute of Technology Sea Grant Program; 1977. 19. Easton R. Black Tide: The Santa Barbara Oil Spill and Its Consequences. New York: Delacorte Press; 1999. 20. Hayes MO. Black Tides. Austin: University of Texas Press; 1999. 21. Etkin DS. Analysis of Oil Spill Trends US and Worldwide. IOSC 2001;291. 22. Etkin DS. Analysis of Past Marine Oil Spill Rates and Trends for Future Contingency Planning. AMOP 2002;227. 23. Etkin DS. Analysis of US Oil Spill Trends to Develop Scenarios for Contingency Planning. IOSC 2003;47. 24. National Research Council. Petroleum in the Marine Environment. Washington, DC: National Academy of Sciences; 1975. 25. Kornberg H. Royal Commission on Environmental Pollution: 8th Report. London: Her Majesty’s Stationery Office; 1981. 26. Baker JM. Impact of Oil Pollution on Living Resources, Comm. Ecology Paper No. 4. Gland, Switzerland: International Union for Conservation of Nature and Natural Resources; 1983. 27. National Research Council. Oil in the Sea: Inputs, Fates, and Effects. Washington, DC: National Academy Press; 1985. 28. GESAMP (IMO/FAO/UNESCO/WMO/WHO/IAEA/UN/UNEP Joint Group of Experts on the Scientific Aspects of Marine Pollution). Impact of Oil and Related Chemicals and Wastes on the Marine Environment. GESAMP Reports and Studies 1993;Vol. 50. 29. Etkin DS. Analysis of US Oil Spillage. American Petroleum Institute Publication 356, Environmental Research Consulting; 2009. 30. Wake H. Oil Refineries: A Review of Their Ecological Impacts on the Aquatic Environment. Estuarine, Coastal and Shelf Science 2005;131. 31. CONCAWE, Trends in Oil Discharged in Aqueous Effluents from Oil Refineries in Europe: 2000 Survey, Report No. 4/04, CONCAWE (The Oil Companies’ European Association for Environmental, Health, and Safety in Refining and Distribution), Brussels, 2004. 32. American Petroleum Institute (API). Water Reuse Studies. Washington, DC: API Publication No. 949. American Petroleum Institute; 1977. 33. Etkin DS. Worldwide Analysis of In-Port Vessel Operational Lubricant Discharges and Leakages. AMOP; 2010. 34. Canadian Environmental Assessment Agency (CEAA), Military Flying Activities in Labrador and Quebec, Ottawa, 1995. 35. Symons L, Hodges MK. Undersea Pollution Threats and Trajectory Modeling. Mar. Techn, Soc. J. 2004;78. 36. Nawadra S, Gilbert TD. Risk of Marine Spills in the Pacific Island Region and Its Evolving Response Arrangements. Sydney, Australia: Proceedings of the International Spill Conference, SpilCon 2002; 2002. 37. South Pacific Regional Environment Programme (SPREP). Regional Strategy to Address Marine Pollution from World War II Shipwrecks. Majuro, Marshall Islands: Thirteenth SPREP Meetings of Officials (Item 7.2.2.1); July 2002, 21e25. 38. Michel J, Etkin DS, Gilbert T, Urban R, Waldron J, Blocksidge CT. Potentially Polluting Wrecks in Marine Waters. IOSC; 2005. 39. Etkin DS, van Rooij JAC, French-McCay D. Risk Assessment Modeling Approach for the Prioritization of Oil Removal Operations from Sunken Wrecks. Interspill; 2009.

48

PART | I

Introduction and the Oil Spill Problem

40. Etkin DS. Cost-Benefit Analyses for Wreck Oil Removal Projects, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 41. Etkin DS. Magnitude of Worldwide Potentially-Polluting Wreck Problem, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 42. Hassello¨v I-M, Morrison G, Rose´n L, Dahllo¨f I, Lindgren F, Knutsson J. Development of a Protocol for Risk Assessment of Potentially Polluting Shipwrecks in Scandinavian Waters, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 43. Cabioc’h F. The Wreck Concern in France and European Waters: Prioritization, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 44. Westerholm D. Repercussions of a Reactive Strategy and Need for a Proactive Strategy, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009.

Part II

Types of Oils and Their Properties

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Chapter 3

Introduction to Oil Chemistry and Properties Merv Fingas

Chapter Outline 3.1. Introduction 3.2. The Composition of Oil

51 51

3.3. Properties of Oil

54

3.1. INTRODUCTION Oil is a general term that describes a wide variety of natural substances of plant, animal, or mineral origin, as well as a range of synthetic compounds.1 This section covers mineral oil or petroleum oil. The many different types of crude oil are made up of hundreds of major constituents and thousands of minor ones. As their composition varies, each type of oil or petroleum product has certain unique characteristics or properties. These properties influence how the oil behaves when it is spilled and determines the fate and effects of the oil in the environment. These properties also influence the efficiency of cleanup operations. This section deals specifically with crude oils and petroleum products derived from crude oils and describes the chemical composition and physical properties.

3.2. THE COMPOSITION OF OIL Crude oils are mixtures of hydrocarbon compounds ranging from smaller, volatile compounds to very large, nonvolatile compounds.2 This mixture of compounds varies according to the geological formation of the area in which the oil is found and strongly influences the properties of the oil. For example, crude oils that consist primarily of large compounds are viscous and dense. Petroleum products such as gasoline or diesel fuel are mixtures of fewer compounds, and thus their properties are more specific and less variable. Crude oil contains many compounds of different sizes and different classes. In fact, Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10003-6 Copyright Ó 2011 Elsevier Inc. All rights reserved.

51

52

PART | II

Types of Oils and Their Properties

there are so many that as time goes by more and more compounds are identified in oil.3 Figure 3.1 shows the number of compounds that are identified and quantified in oils by year as well as the prediction for the future. Some analysts have preliminarily identified up to 17,500 compounds in an oil. Hydrocarbon compounds are composed of hydrogen and carbon, which are therefore the main elements in oils. Oils also contain varying amounts of sulphur, nitrogen, oxygen, and sometimes mineral salts, as well as trace metals such as nickel, vanadium, and chromium. In general, the hydrocarbons found in oils are characterized by their structure. A common and older method of classification is by SARA dsaturates, aromatics, resins, and asphaltenes. Figure 3.2 illustrates the SARA classification along with classes of compounds typically found in this overall classification. The saturate group of components in oils consists primarily of alkanes, which are compounds of hydrogen and carbon with the maximum number of hydrogen atoms around each carbon. Thus, the term saturate is used because the carbons are “saturated” with hydrogen. The saturate group includes straight-chain alkanes and branched-chain alkanes and also includes cycloalkanes, which are compounds made up of the same carbon and hydrogen constituents, but with the carbon atoms bonded to each other in rings or circles. Straight-chain saturate compounds from C18 and up are often referred to as waxes. The aromatic compounds include at least one benzene ring of six carbons. Three carbon-to-carbon double-bonds float around the ring and provide

Compounds Identified

4000

3000

Prediction

2000

1000

0 1970

1980

1990

2000

Year

2010

2020

FIGURE 3.1 The number of compounds identified and quantified in crude oils by year, including prediction in the future.

Chapter | 3

53

Introduction to Oil Chemistry and Properties

Groupings

Example Classes, Names, and Compounds

Saturates

alkanes

Chemical class Alternate name

Description

paraffins

Example compound

dodecane C12H26

cycloalkanes waxes

Aromatics

naphthanates

decalin n-alkanes C18-C80

Benzenes BTEX

benzene Benzene, Toluene, Ethylbenzene, Xylenes

PAHs

Naphthenoaromatics

Resins Asphaltenes

anthracene

combinations of aromatics and cycloalkanes

tetralin

class of mostly anomalous polar compounds carbazole sometimes containing oxygen, nitrogen, sulphur, or metals

N

class of large anomalous compounds structures not known sometimes containing oxygen, nitrogen, metals, or sulphur

FIGURE 3.2 An overview of the classification of compounds with specific examples.

stability. Because of this stability, benzene rings are very persistent and can have toxic effects on the environment. The most common smaller aromatic compounds found in oil are often referred to as BTEX, or Benzene, Toluene, Ethyl-benzene, and Xylenes. Polyaromatic hydrocarbons or PAHs are compounds consisting of at least two benzene rings. PAHs make up between 0 and 60% of the composition of oil. The olefins, or unsaturated compounds, are another group of compounds that contain less hydrogen atoms than the maximum possible. Olefins have at least one double carbon-to-carbon bond that displaces two hydrogen atoms. Significant amounts of olefins are found only in refined products. Polar compounds are those that have a significant molecular charge as a result of bonding with compounds such as sulphur, nitrogen, or oxygen. The “polarity” or charge that the molecule carries results in behavior that may be different from that of other compounds. In the petroleum industry, the smallest polar compounds are called resins, which are largely responsible for oil adhesion. The larger polar compounds are called asphaltenes because they often make up the largest percentage of the asphalt commonly used for road construction. Asphaltenes often have very large molecules and, if in abundance in an oil, they have a significant effect on oil behavior.4 Bitumen, which comes from heavy oil deposits or tar sands, consists largely of asphaltenes that must be broken down to smaller compounds before refining. Crude oil is processed in refineries to yield petroleum products that are used for heating, transport, and chemical synthesis. Table 3.1 lists some of the

54

PART | II

Types of Oils and Their Properties

TABLE 3.1 General Characterizations of Product Distillation Ranges Product

Distillation Temperature Range ( C)

Approximate Carbon Number Range

Gasoline

30e200

5e12

Naphtha

100e200

8e12

Jet Fuel & Kerosene

150e250

11e13

Diesel fuel

160e400

13e17

Gas-Oil

220e350

Heavy fuel oils

315e540

20e45

Atmospheric residue

>450

30þ

Vacuum residue

>600

60þ

products produced by distillation, a primary refinery process. Table 3.2 gives the general composition of some typical fuels and oils.5 The following are the oils or fuels that be used to illustrate the fate, behavior, and cleanup of oil spills: l l l l l

l

gasolinedas used in automobiles diesel fueldas used in trucks, trains, and buses a light crude oil a heavy crude oil an intermediate fuel oil (IFO)da mixture of a heavy residual oil and diesel fuel used primarily as a propulsion fuel for ships (the intermediate refers to the fact that the fuel is between a diesel and a heavy residual fuel) bunker fueldsuch as Bunker C, which is a heavy residual fuel remaining after the production of gasoline and diesel fuel in refineries and often used in heating plants

3.3. PROPERTIES OF OIL The properties of oil discussed here are viscosity, density, specific gravity, solubility, flash point, pour point, distillation fractions, interfacial tension, and vapor pressure. These properties for the oils noted as examples above are listed in Table 3.3.5 Viscosity is the resistance to flow in a liquid. The lower the viscosity, the more readily the liquid flows. For example, water has a low viscosity and flows readily, whereas honey, with a high viscosity, flows slowly. The viscosity of the

(% e except for metals) Group

Compound Class

alkanes cyclo-alkanes

Diesel

Light Crude

50 to 60

65 to 95

55 to 90

25 to 80

25 to 35

20 to 30

45 to 55

35 to 45

0 to 20

0 to 10

2 to 10

5 to 15

5

waxes Olefins Aromatics BTEX

IFO

Bunker C

30 to 50 0 to 1

5 to 10

0 to 10

25 to 40

5 to 25

10 to 35

15 to 40

40 to 60

30 to 50

15 to 25

0.5 to 2.0

0.1 to 2.5

0.01 to 2.0

0.05 to 1.0

0.00 to 1.0

0 to 5

10 to 35

15 to 40

30 to 50

30 to 50

0 to 2

1 to 15

5 to 40

15 to 25

10 to 30

0 to 2

0 to 10

2 to 25

10 to 15

10 to 20

0 to 10

0 to 20

5 to 10

5 to 20

30 to 250

100 to 500

100 to 1000

100 to 2000

0 to 2

0 to 5

0.5 to 2.0

2 to 4

PAHs Polar Compounds resins asphaltenes Metals (in parts per million) Sulphur

Heavy Crude

0.02

0.1 to 0.5

Introduction to Oil Chemistry and Properties

Saturates

Gasoline

Chapter | 3

TABLE 3.2 Typical Composition of Some Oils and Petroleum Products

55

56

TABLE 3.3 Typical Oil Properties Property Viscosity

Units

mPa.s at 15 C

Gasoline

Diesel

0.5

2

Light Crude 5 to 50

Heavy Crude 50 to 50,000

0.72

0.84

0.78 to 0.88

0.88 to 1.00

Flash Point

C

35

45

30 to 30

30 to 60

Solubility in Water

ppm

200

40

10 to 50

Pour Point

NR

35 to 10 40 to 30

65

35

27

100 C 200 C

C

API Gravity Interfacial Tension

mN/m at 15 C

1000 to 15,000

10,000 to 50,000

0.94 to 0.99

0.96 to 1.04

80 to 100

>100

5 to 30

10 to 30

1 to 5

40 to 30

10 to 10

5 to 20

30 to 50

10 to 30

10 to 20

5 to 15

27

10 to 30

15 to 30

25 to 30

25 to 35

70

1

2 to 15

1 to 10

e

e

100

2 to 5

2 to 5

Distillation Fractions % distilled at

30

15 to 40

2 to 25

85

30 to 60

15 to 45

15 to 25

5 to 15

100

45 to 85

25 to 75

30 to 40

15 to 25

15 to 55

25 to 75

60 to 70

75 to 85

300 C 400 C residual NR ¼ not relevant

Types of Oils and Their Properties

g/mL at 15 C

Bunker C

PART | II

Density

Intemediate Fuel Oil

Chapter | 3

Introduction to Oil Chemistry and Properties

57

oil is largely determined by the amount of lighter and heavier fractions that it contains. The greater the percentage of light components, such as small saturates, and the lesser the amount of asphaltenes, the lower the viscosity. As with other physical properties, viscosity is affected by temperature, with a lower temperature giving a higher viscosity. For most oils, the viscosity varies as the logarithm of the temperature, which is a very significant variation. Oils that flow readily at high temperatures can become a slow-moving, viscous mass at low temperatures. In terms of oil spill cleanup, viscosity can affect the oil’s behavior. Viscous oils do not spread rapidly, do not penetrate soil as readily, and are difficult to pump and skim. Density is the mass (weight) of a given volume of oil and is typically expressed in grams per cubic centimeter (g/cm3). It is the property used by the petroleum industry to define light or heavy crude oils. Density is also important as it indicates whether a particular oil will float or sink in water. As the density of fresh water is 1.0 g/cm3 at 15 C and the density of most oils ranges from 0.7 to 0.99 g/cm3, most oils will float on water. As the density of seawater is 1.03 g/cm3, even heavier oils will usually float on it. The density of oil increases with time, as the light fractions evaporate. Occasionally, when the density of an oil becomes greater than the density of freshwater or seawater, the oil will sink. Sinking is rare, however, and happens only with a few oils, usually residual fuels such as Bunker C. Significant amounts of oil have sunk in only about 25 incidents out of thousands. However, as heavier and heavier oils are being used more frequently, this may become more common in the future. Another measure of density is specific gravity, which is an oil’s relative density compared to that of water. If the oil-specific gravity is greater than 1, it sinks; if it is less than 1, it floats. Another gravity scale is that of the American Petroleum Institute (API). The API gravity is based on the density of pure water that has an arbitrarily assigned API gravity value of 10 (10 degrees). Oils with progressively lower specific gravities have higher API gravities. The following is the formula for calculating API gravity: API gravity ¼ [141.5 O (oil density at 15.5 C)] 131.5. Oils with high densities have low API gravities and vice versa. Solubility in water is the measure of how much of an oil will dissolve in the water column on a molecular basis. Solubility is important in that the soluble fractions of the oil are sometimes toxic to aquatic life, especially at higher concentrations. As the amount of oil lost to solubility is always small, this is not as great a loss mechanism as evaporation. In fact, the solubility of oil in water is so low (generally less than 100 parts per million) that it would be the equivalent of approximately one grain of sugar dissolving in a cup of water. Yet, even this small amount is important to the environment as even small amounts may be toxic to certain biota. The flash point of an oil is the temperature at which the liquid gives off sufficient vapors to ignite upon exposure to an open flame. A liquid is considered to be flammable if its flash point is less than 60 C. There is a broad range of flash points for oils and petroleum products, many of which are

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Types of Oils and Their Properties

considered flammable, especially when fresh. Gasoline, which is flammable under all ambient conditions, poses a serious hazard when spilled. Many fresh crude oils have an abundance of volatile components and may be flammable for as long as one day until the more volatile components have evaporated. On the other hand, Bunker C and heavy crude oils generally are not flammable even when spilled. The pour point of an oil is the temperature at which it takes longer than a specified time to pour from a standard measuring vessel. As oils are made up of hundreds of compounds, some of which may still be liquid at the pour point, the pour point is not the temperature at which the oil will no longer pour. The pour point represents a consistent temperature at which an oil will pour very slowly and therefore has limited use as an indicator of the state of the oil. In fact, pour point has been overused in the past to predict how oils will behave in the environment. For example, waxy oils can have very low pour points, but may continue to spread slowly at that temperature and can evaporate to a significant degree. It is important to note that pour point is not the solidification temperature. As produced crude oils become heavier, pour point becomes less relevant. Distillation fractions of an oil represent the fraction (generally measured by volume) of an oil that is boiled off at a given temperature. This data is obtained on most crude oils so that oil companies can adjust parameters in their refineries to handle the oil. This data also provides environmentalists with useful insights into the chemical composition of oils. For example, while 70% of gasoline will boil off at 100 C, only about 5% of a crude oil will boil off at that temperature and an even smaller amount of a typical Bunker C. The distillation fractions correlate strongly to the composition as well as to other physical properties of the oil. Equations to predict evaporation can use distillation fraction data as input. The oil/water interfacial tension, sometimes called surface tension, is the force of attraction or repulsion between the surface molecules of oil and water. Together with viscosity, surface tension is an indication of how rapidly and to what extent an oil will spread on water. The lower the interfacial tension with water, the greater the extent of spreading. In actual practice, the interfacial tension must be considered along with the viscosity because it has been found that interfacial tension alone does not account for spreading behavior. The vapor pressure of an oil is a measure of how the oil partitions between the liquid and gas phases, or how much vapor is in the space above a given amount of liquid oil at a fixed temperature. Because oils are a mixture of many compounds, the vapor pressure changes as the oil weathers. Vapor pressure is difficult to measure and is not frequently used to assess oil spills. Again as oil is a mixture of hundreds of compounds, vapor pressure is not entirely relevant. Although there is a high correlation between the various properties of an oil, these correlations should be used cautiously as oils vary so much in composition. For example, the density of many oils can be predicted based on their

Chapter | 3

Introduction to Oil Chemistry and Properties

59

viscosity. For other oils, however, this could result in errors. For example, waxy oils have much higher viscosities than would be implied from their densities. There are several mathematical equations for predicting one property of an oil from another property, but these must be used carefully as there are many exceptions.

REFERENCES 1. Neumann H-J, Paczynska-Lahme B, Severin D. Composition and Properties of Petroleum. New York: Halsted Press; 1981. 2. Speight JG. The Chemistry and Technology of Petroleum. 4th ed. Boca Raton, FL: CRC Press; 2007. 3. Marshall AG, Hendrickson CL. High-Resolution Mass Spectrometers, chapter in Annual Review of Analytical Chemistry, Volume 1, 2008, Young ES and Zare RN, editors., Annual Reviews, Palo Alto, CA, p. 579e99, 2008. 4. Groenzin H, Mullins OC. Asphaltene Molecular Size and Weight by Time-Resolved Fluorescence Depolarization, Chapter 2 in Asphaltenes, Heavy Oils and Petroleomics. In: Mullins OC, Sheu EY, Hammami A, Marshall AG, editors. New York: Springer Publications; 2007. p. 17. 5. Fingas MF. The Basics of Oil Spill Cleanup. 2nd ed. Boca Raton, FL: CRC Press; 2000.

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Part III

Oil Analysis and Remote Sensing

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Chapter 4

Measurement of Oil Physical Properties Bruce Hollebone

Chapter Outline 4.1. Introduction 4.2. Bulk Properties of Crude Oil and Fuel Products 4.3. Hydrocarbon Groups 4.4. Quality Assurance and Control

63 63 73 77

4.5. Effects of Evaporative Weathering on Oil Bulk Properties Appendix 4.1

78

85

4.1. INTRODUCTION During any uncontrolled release of oil, the properties of the spilled oil, including the bulk physical property changes due to weathering, must be immediately available, so that models can be used to predict the environmental impacts of the spill and guide the selection of various remediation alternatives. Unfortunately, the properties routinely measured by oil producers and refiners are not the ones that spill responders need to know most urgently. Questions important to responders include the following: l l

l l l l

the physical properties of the oil and how these change over time how the compositional and bulk property changes affect an oil’s behavior and fate whether emulsions will form whether the oil is likely to submerge the hazard to on-site personnel during cleanup the oil toxicity to marine or aquatic organisms

4.2. BULK PROPERTIES OF CRUDE OIL AND FUEL PRODUCTS The physical properties of the almost limitless variety of crude oils are generally correlated with aspects of chemical composition. Some of these key Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10004-8 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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properties for determining the fate and behavior of oil and petroleum products in the environment are viscosity, density, specific gravity (density relative to water), flash point, pour point, distillation, and interfacial tension. These properties for the oils are listed in Table 4.1. Viscosity is the resistance to flow in a liquid. The lower the viscosity, the more readily the liquid flows. The viscosity of an oil is a function of its composition; therefore, crude oil has a wide range of viscosities. For example, the viscosity of Federated oil from Alberta is 5 mPa$s, while a Sockeye oil from California is 45 mPa$s at 15 C . In general, the greater the fraction of saturates and aromatics and the lower the amount of asphaltenes and resins, the lower the viscosity. As oil weathers, the evaporation of the lighter components leads to increased viscosity. As with other physical properties, viscosity is affected by temperature, lower temperatures giving higher viscosities. For most oils, the viscosity varies approximately exponentially with temperature. Oils that flow readily at high temperature can become a slow-moving, viscous mass at low temperature. In terms of oil spill cleanup, viscous oils do not spread rapidly, do not penetrate soils readily, and affect the ability of pumps and skimmers to handle the oil. The dynamic viscosity of an oil can be measured by a viscometer using a variety of standard cup-and-spindle sensors at controlled temperatures. Density is the mass of a unit volume of oil, usually expressed as grams per millilitre (g/mL) or, equivalently, as kilograms per cubic metre (kg/m3). It is used by the petroleum industry to grade light or heavy crude oils. Density is also important because it indicates whether a particular oil will float or sink in water. As the density of water is 1.0 g/mL at 15 C and the density of most oils ranges from 0.7 to 0.99 g/mL, oils typically float on water. As the density of seawater is 1.03 g/mL, even heavier oils will usually float on it. Only a few bitumens have densities greater than water at higher temperatures. However, as water has a minimum density at 4 C and oils will continue to contract as temperature decreases, heavier oils, including heavy crudes and residual fuel oils, may sink in freezing waters. Furthermore, as density increases as the light ends of the oil evaporate off, a heavily weathered oil, long after a spill event, may sink or be prone to overwashing, where the fresh oil, immediately after the spill, may have floated readily. A related measure is specific gravity, an oil’s density relative to that of water. As the densities of both water and oil vary differently with temperature, this quantity can be highly variable. The American Petroleum Institute (API) uses the specific gravity of petroleum at 50 F (15.56 C) as a quality indicator for oil. Pure water has an API gravity of 10. Oils with progressively lower specific gravities have higher API gravities. Heavy, inexpensive oils have less than 25 API; medium oils are 25 to 35API; and light commercially valuable oils are 35 to 45API. API gravities generally vary inversely with viscosity and asphaltene content. Interfacial tensions are the net stresses at the boundaries between different substances. They are expressed as the increased energy per unit area (relative to the bulk materials), or equivalently as force per unit length. The ‘Standard

Chapter | 4

Intermediate Fuel Oil

Bunker C

Crude Oil Emulsion

1,000 to 15,000

10,000 to 50,000

20,000 to 100,000

0.88 to 1.00

0.94 to 0.99

0.96 to 1.04

30 to 50

10 to 30

10 to 20

5 to 15

10 to 15

10 to 30

15 to 30

25 to 30

25 to 35

N/A

80 to 100

>100

>80

5 to 20

>50

Property

Units

Gasoline

Diesel

Light Crude

Viscosity

m.Pa$s

0.5

2

5 to 50

Density

g/mL

0.72

0.84

0.78 to 0.88

50 to 65

35 to 40

27

27

API Gravity Interfacial Tension

mN/m

Heavy Crude 50 to 50,000

Flash Point

C

35

55 to 65

30 to 30

30 to 60

Pour Point

C

N/A

60

55 to 0

30 to 30

10 to 10

0.95 to 1.0

Measurement of Oil Physical Properties

TABLE 4.1 Typical Oil and Fuel Properties at 15 C

65

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International (SI)’ units for interfacial tension are milliNewtons per meter (mN/ m). Surface tension is thought to be related to the final size of a slick. The lower the interfacial tension of oil with water, the greater the extent of spreading and thinner terminal thickness of oil. In actual practice, the interfacial tension alone does not apparently account for spreading behavior; environmental effects and other effects seem to be dominant. The flash point of an oil is the temperature at which the vapor over the liquid can be ignited. A liquid is considered to be flammable if its flash point is less than 60 C. Flash point is an important consideration for the safety of spill cleanup operations. Gasoline and other light fuels can ignite under most ambient conditions and therefore are a serious hazard when spilled. Many freshly spilled crude oils also have low flash points until the lighter components have evaporated or dispersed. On the other hand, Bunker C and heavy crude oils generally are not flammable when spilled. The pour point of an oil is the temperature at which no flow of the oil is visible over a period of 5 seconds from a standard measuring vessel. The pour point of crude oils ranges from 60 C to 30 C. Lighter oils with low viscosities generally have lower pour points. As oils are made up of hundreds of compounds, some of which may still be liquid at the pour point, the pour point is not the temperature at which an oil will no longer pour. The pour point represents a consistent temperature at which an oil will pour very slowly and therefore has limited use as an indicator of the state of the oil. For example, waxy oils can have a very low pour point, but may continue to spread slowly at that temperature and can evaporate to a significant degree.

4.2.1. Density and API Gravity The density of an oil sample, in g/mL, is best measured using a digital density meter following American Society for Testing and Materials (ASTM) method D 5002.1 The instrument is calibrated using air and distilled, deionized water. Acoustically measured densities must be corrected for sample viscosity, as specified by the instrument manufacturer. API gravity (API 82) is calculated using the specific gravity of an oil at 60 F (15.56 C).2 The oil density at 15.56 C can be estimated by exponential extrapolation from the higher (THi) and lower (TLo) data points, if necessary. This is converted to specific gravity by division by the density of water at 15.5 C, using the following equation: s:g:15:56 ¼ rTHi exp

h

.

THi TLo i. THi 15:56 þ In rTHi rðH2 OÞ15:56 In rTHi In rTLo

(1)

where s.g.15.56 is the specific gravity of the oil or product at 15.56 C (60 F), rTLo and rTHi are the measured oil densities at TLo and THi, respectively, and

Chapter | 4

Measurement of Oil Physical Properties

67

r(H2O)15.56 is the density of water at 15.56 C. The API gravity is then determined using the formula (API 82): API ¼ 141:5= s:g:15:56 131:5 (2)

4.2.2. Dynamic Viscosity The dynamic viscosity of an oil sample, in mPa$s or cP, is measured using an enclosed spinning cup viscometer using standard NV and SV1 cup-and-spindle sensors.3 Check standards of pure ethylene glycol and glycerine can be conveniently used to validate the NV and SV1 methods, respectively. From a qualitative observation of the oil, either the NV or the SV1 sensor is chosen to measure the sample. The NV sensor is used for oils with viscosities below 100 mPa$s, and the SV1 sensor, for oils above 70 mPa$s to 10,000 mPa$s. For oils with higher viscosity, measurements must be made on cone and plate or parallel plate instruments (see below). For both cases using the rotary viscometer, the measurement cup is filled with a sample to the edge or the rotating surface. The sensor is mounted onto the instrument, and the sample volume is adjusted to the proper level. The sample is allowed to equilibrate until the sample temperature probe stabilizes at the measurement temperature and remains stable for 5 minutes. Samples and sensors are kept chilled at the appropriate temperature prior to use. For the NV sensor, the rotational shear rate is set at 1,000/s, the SV1 sensor at 50/s. If the oil is observed to be non-Newtonian, single samples are run at shear rates of 1/s, 10/s, and 100/s. In all cases, the sensors are ramped up to speed over a period of 5 minutes. The viscosity is measured for a subsequent 5 minutes, sampled once per second. The viscosity reported is that at time zero of the second, constant-shear rate interval. This may be obtained by the mean of the constant-shear rate interval data or by linear fit to the time-viscosity series if friction-heating has occurred during the measurement. For Newtonian samples, triplicate measurements are averaged and the mean is reported as the absolute or dynamic viscosity. For non-Newtonian samples, viscosities are reported for each of the three shear rates. Viscosities above 50,000 mPa$s are measured on a parallel plate rheometer with an air bearing. Measurement for most oils can be performed with a 35 mm plate/plate geometry at a gap of 2 mm between plates. A stress sweep in forced oscillation mode at 1 Hz performed over an appropriate range will determine the stress independent regions. A creep test can then be performed at a stress value selected in the stable “sol” range of flow response for the material. This provides the zero shear viscosity value.

4.2.3. Surface and Interfacial Tensions Surface and interfacial tensions, in mN/m, are normally determined by one of two methods. The de No€ uy ring is a common technique, used by many laboratories,

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Oil Analysis and Remote Sensing

and has been codified as ASTM method D 971.4 It depends on accurate measurement of the maximum force that a platinum ring can exert on the surface of a liquid before detachment. A second emerging technique that shows much promise for improved speed and accuracy is the pendant/rising drop method, which depends on shape calculations of a droplet of oil in air or water.5,6 The values that are important for spill responders include the oil/air, oil/ water, and the oil/seawater interfacial tensions. The oil/air interfacial tension is often called surface tension. As interfacial tensions are temperature dependent, it is often convenient to determine these quantities for several temperatures. Two measurements at freezing, 0 C, and at ambient temperature, 25 C, allow for a wide range of interpolated values. Measurement at 50 F/15 C also allows determination of common marine temperatures.

€ Ring Determination of Interfacial Tensions De Nouy A measurement apparatus specific to the de No€ uy ring test is required. Manual machines are common, but automated systems are now available that make measurements much quicker and repeatable. All measurement equipment, rings, measurement vessels, transfer, and storage containers must be scrupulously clean before measurement. Surface and interfacial tension measurements are very sensitive to contamination by organic chemicals or salts. For sample/air surface tensions, the instrument is zeroed with the measurement ring in the air. A small amount of sample, approximately 15 mL, is poured into a vessel of sufficient diameter that the wall effects on the meniscus do not affect the area through which the ring will pass. The ring is dipped into the sample to a depth of no more than 5 mm and is then pulled up such that it is just visible on the surface of the liquid. The system is allowed to rest for 30 seconds. The measurement is initiated, terminating when the upward pulling force on the ring just balances the downward force exerted by the liquid. The apparent surface tension, sAPP, is recorded. For sample/water and sample/brine interfacial tensions, the ring is zeroed in the sample at a depth of not more than 5 mm. The ring is removed and cleaned. A volume of water or brine is dispensed into the measurement vessel. The ring is dipped 5 mm into the aqueous phase. A small volume of sample is carefully poured down the side of the vessel wall, with great care taken so as to disturb the aqueous/oil interface as little as possible. The overlying layer should be at least 5 mm thick. The ring is then raised to the bottom on the interface, and the system is allowed to rest for exactly 30 seconds. The measurement is started, and the apparent interfacial tension is recorded, sAPP, when the force balance is reached. The apparent surface tension is corrected for mass of the upper phase lifted by the ring during measurement using the Zuidema and Waters6 correction: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 1:452sAPP 1:679 þ 0:04534 (3) s ¼ sAPP 0:7250 þ C2 ðD dÞ R=r

Chapter | 4

Measurement of Oil Physical Properties

69

where s is the interfacial tension, sAPP is the instrument scale reading, C is the ring diameter, D is the density of the lower phase, d is the density of the upper phase, R is the radius of the du No€ uy ring, and r is the radius of the ring wire. As these measurements depend on temperature, samples, aqueous phases and glassware should be kept at the measurement temperature for a minimum of 30 minutes before a determination is made.

Pendant/Rising Drop Determination of Interfacial Tensions In this test, the interfacial tension is determined by calculation with comparison to the shape of a drop hanging from the end of a needle. A camera is used to photograph a picture of a drop hanging from a needle. The digital picture is analyzed by software; then a parameterized curve shape is developed, from which the surface tension is calculated.6 In the case of a liquideliquid interfacial tension, the surrounding fluid must be clear, so that a good image may be generated. For oil in water, this requires that the oil be suspended in water. However, as most oils are less dense than water, the rising oil bubble, rather than the pendant drop, must be measured. In this case, the image is inverted in software and, instead of the force of gravity, the buoyant force, determined as the fraction of gravity based on the specific gravity of the oil is used: b ¼ gðrwater roil Þ=rwater

(4)

where b is the buoyant force, g is the acceleration due to gravity, rwater is the density of water at the measurement temperature, and roil is the oil density.

4.2.4. Flash Point The flash point of an oil product can be determined by several methods, depending on the oil product and the quantity available. Lower viscosity products, including light fuel oils and most fresh crudes, are measured by the Tag closed-cup method. This follows ASTM method D 1310.7 Though accurate, the Tag method uses a comparatively large volume of oil, 50 to 70 mL. Smaller volumes, 1e2 mL, can be measured by ASTM D6450.8 The practical working range of these two methods is e10 C to approximately 100 C. With subambient cooling, using dry ice baths and/or liquid nitrogen baths, much lower flash point temperatures can be measured, but this is often not necessary for emergency response considerations. Heavier products, including intermediate and heavy fuel oils, can be measured by a Pensky-Martins analyzer, following ASTM D 93.9 As with the Tag method, this method uses 50e70 mL of crude oil. Smaller volumes can be used with the newer method ASTM D7094, which uses only 2 mL of oil.10 The working range for these heavier type tests is approximately 50 C to 225 C.

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PART | III

Oil Analysis and Remote Sensing

The standard test material for assuring quality control for a lowertemperature flash point apparatus historically has been para-xylene; however, heavier normal alkane standards, n-decane, n-undecane, n-tetradecane, and n-hexadecane have also been found to be suitable and offer a wider range of test temperatures.11

4.2.5. Pour Point The pour point of an oil sample, in degrees Celsius, can only be determined by following ASTM method D 97.12 Sample aliquots are poured into ASTMapproved jars, stopped and fixed with ASTM-certified thermometers. The temperature regime described in the standard is critical; particularly in waxy oils, with high normal alkane contents, a crust of waxy crystals can form on the surface of the oil as it cools. The ASTM D 97 heating and cooling process for oil is designed to ensure that the formation of these microstructures does not interfere with reproducible measurement of the pour point.

4.2.6. Sulphur Content The mass fraction of atomic sulphur in oil is conveniently determined using X-ray fluorescence closely following ASTM method D 4294.13 In brief, the method is as follows: approximately 3 g of oil is weighed out into standard 31 mm XRF cells. The sealed cells are then measured in an XRF spectrometer. The spectrometer response is calibrated using a series of certified reference material standards. Spectra should be corrected for interference by chlorine by subtraction, based on a calibration curve established by the certified reference materials. Matrix effects, X-ray absorption by the base oil, can be corrected by subtraction of a spectrum of an oil free of sulphur, such as a mineral or lubricating oil.

4.2.7. Water Content The mass fraction of water in oil or an emulsion, expressed as a percentage, is best determined by Karl Fischer titration, using ASTM method D 4377.14 The Karl Fischer reaction is an amine-catalyzed reduction of water in a methanolic solution: CH3 OH þ SO2 þ RN/½RNHþ þ ½SO3 CH3 2RN þ H2 O þ I2 þ ½RNHþ ½SO3 CH3 /½RNHþ ½SO4 CH3 þ 2½RNHþ I

(5)

The amine, RN, or mixture of amines is proprietary to each manufacturer. An aliquot of approximately 1 g of oil is accurately weighed, then introduced to the reaction vessel of the autotitrator. A solution of 1:1:2 (by volume) mixture of methanol:chloroform:toluene is used as a working fluid.

Chapter | 4

Measurement of Oil Physical Properties

71

4.2.8. Evaluation of the Stability of Emulsions Formed from Brine and Oils and Oil Products Water-in-oil emulsions are formed in 2.2-liter fluorinated vessels on an endover-end rotary mixer at a rotational speed of 50 RPM.15,16 1. 600 mL of salt water (3.3% w/v NaCl) is placed in each mixing vessel. 2. 30 mL of oil is added to each vessel for a 1:20 oil:water ratio. 3. The vessels are sealed and placed in the rotary mixer such that the cap of each mixing vessel follows, rather than leads, the direction of rotation. The rotary mixer is kept in a temperature-controlled cold room at 15 C. 4. The vessels and their contents are allowed to stand for approximately 4 hours before rotation begins, then mixed continuously for 12 hours. 5. At the conclusion of the mixing time, the emulsions are collected from the vessels for measurement of water content, viscosity, and the complex modulus. The emulsions are stored at 15 C for one week, then observed for changes in physical appearance. Water content for the emulsions should be determined. The Karl-Fischer titration method works well for all types of emulsion and watereoil mixtures. The complex modulus of the emulsion is measured on a rheometer using a 35 mm plate-plate geometry. A stress sweep is performed in the range 100 to 10,000 mPa in the oscillation mode at a frequency of 1 Hz. The complex modulus value in the linear viscoelastic region is reported.

4.2.9. Evaluation of the Relative Dispersability of Oil and Oil Products This method determines the relative ranking of effectiveness for the dispersibility of an oil sample by to a dispersant test mixture. It is used either to determine the effectiveness of a dispersant product for a standard crude oil or to test the dispersability of a crude oil against a standard dispersant. This method follows ASTM F 2059 closely.17 A premix of 1:25.0 dispersant:oil is made up by adding oil to 100 mg of dispersant (approximately 2.50 mL of oil in total). Six ASTM-standard swirling conical flasks modified with side spouts, containing 120 mL of 33& brine, are placed into an incubator-shaker. An aliquot of 100 mL of premix is added to the surface of the liquid in each flask, care being taken not to disturb the bulk brine. The flasks are mechanically shaken at 20.0 C with a rotation speed of 150 rpm for exactly 20 minutes. The solutions are allowed to settle for 10 minutes. Using the side spout, 30 mL of the oil-in-water phase is transferred to a 250 mL separatory funnel, first clearing the spout by draining 3 mL of liquid. The 30 mL aliquot is extracted with 35 mL of 70:30 (v:v) dichloromethane:pentane, collected into a 25 mL graduated cylinder.

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PART | III

Oil Analysis and Remote Sensing

A Gas Chromatograph-Flame Ionization Detector (GC/FID) is used to determine the oil concentration in the solvent. A 900 mL aliquot of the 15 mL solvent extract is combined with 100 mL of internal standard (200 ppm of 5-aandrostane in hexane) in a crimp-top injection vial and shaken well. The total petroleum hydrocarbon content of the sample is quantified by the internal standard method using the total resolved peak area and the average hydrocarbon response factor over the entire analytical range: RPH ¼ ATOTAL =AI:S: =RRF 20 15 120=30=0:9

(6)

where RPH is the resolved petroleum hydrocarbon (mg/mL), ATOTAL is the total resolved peak area, AI.S. is the internal standard peak area, and RRF is the relative response factor for a series of alkane standards covering the analytical range. The method is calibrated using a series of six oil-in-solvent mixtures prepared from the premix for each oil. The volume of premix dispersant/oil solution for each standard is selected to represent a percentage efficiency of the dispersed oil. The volume of the premix is then carefully applied to the surface of the brine in a shaker flask and shaken exactly as one of the samples, as described previously. Upon removal from the shaker however, the entire contents of the flask is transferred to the separatory funnel. This is extracted with 3 20 mL of 70:30 (v:v) dichloromethane:pentane and made up to 60 mL. Chromatographic quantitation is then performed using the formula: RPH ¼ ATOTAL =AI:S: =RRF 20 60 120=120=0:9

(7)

The RPH values as a function of % effectiveness for the calibration standards are plotted. The sample RPH values are then used to determine the percentage effectiveness of the dispersant. Note that these effectiveness percentages are not expected to correlate to real-world dispersabilities. It is important to remember that these values are relative rankings only.

4.2.10. Adhesion to Stainless Steel Adhesion to stainless steel is useful to responders in order to judge the “stickiness” of oil to certain drum skimmer configurations. Environment Canada has developed a quantitative test for this purpose.18,19 An analytical balance is prepared by hanging an ASTM method D 6 standard penetrometer needle from the balance hook and allowing the apparatus to stabilize and tare. Approximately 80 mL of oil sample is poured into a 100 mL beaker. The beaker is elevated until the oil reaches the top of the stainless steel needle. Care is taken not to coat the brass segment of the needle. The needle rests for 30 seconds immersed in the oil. The beaker is lowered until the needle is clear of the oil. The system is left undisturbed, closed inside a draft shield. After 30 minutes, the weight of the oil adhering to the needle is recorded. The

Chapter | 4

Measurement of Oil Physical Properties

73

mass of the oil divided by the surface area of the needle is the adhesion of the oil in g/cm2. Typically, four measurements are taken for each oil sample and the mean reported as the final value.

4.3. HYDROCARBON GROUPS The fate and behavior of crude oils and petroleum products are strongly determined by their chemistries. The main constituents of oils can be grouped into four categories: saturated hydrocarbons (including waxes), aromatics, resins, and asphaltenes. Saturates: A group of hydrocarbons composed of only carbon and hydrogen with no double bonds or aromaticity. They are said to be “saturated” with hydrogen. They may by straight-chain (normal), branched, or cyclic. Typically, however, the group of “saturates” refers to the aliphatics generally including alkanes, as well as a small amount of alkenes. The lighter saturates, those less than ~C18, make up the components of an oil most prone to weathering. The larger saturates, generally those heavier than C18, are termed waxes. Aromatics: These are cyclic organic compounds that are stabilized by a delocalized p-electron system. They include such compounds as BTEX (benzene, toluene, ethylbenzene, and the three xylene isomers), polycyclic aromatic hydrocarbons (PAHs, such as naphthalene), and some heterocyclic aromatics such as the dibenzothiophenes. Benzene and its alkylated derivatives can constitute several percent in crude oils. PAHs and their alkylated derivatives can also make up as much as a percent in crude oils. Resins: This is the name given to a large group of polar compounds in oil. They include heterosubstituted aromatics (typically oxygen- or nitrogencontaining PAHs), acids, ketones, alcohols, and monoaromatic steroids. Because of their polarity, these compounds are more soluble in polar solvents than the nonpolar compounds, such as waxes and aromatics, of similar molecular weight. Asphaltenes: A complex mixture of very large organic compounds that precipitate from oils and bitumen by natural processes. For the purposes of this method, asphaltenes are defined as the fraction that precipitates in n-pentane. The separation of petroleum and its products into these four characteristic groups is known as fractionation. The quantification of the groups is often referred to as SARA analysis, an acronym of the characteristic groups: saturates, aromatics, resins, and asphaltenes. Historically, many techniques have been used to perform this separation, including distillation, solvent precipitation (ASTM D6560)20, treatment with strong acids (ASTM D2006)21, adsorption (ASTM D2007 and D4124)22,23, and thin-layer chromatography.24 For reviews of the methods, see Speight and Becker.24-26 While excellent methods for the determination of the SARA groups have been developed using thin-layer chromatograph (TLC), there has been continuing interest in alternate

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test methods based on solvent separation and adsorption techniques.22-24 Gravimetric methods are typically based on the solubilities of the groups in n-pentane, hexane/benzene, and methanol.3 Such methods can rely on gravimetric determinations of all components, including the saturate and aromatic groups. However, the drawback of such methods is that they contain significant volatile components. This is particularly true of crude oils and lighter fuels. More sophisticated methods rely on a combination method involving determination of the saturate and aromatic fractions by gas chromatography, an adaptation of total petroleum hydrocarbon methods, while gravimetrically determining the nonvolatile resin and asphaltene components.27,28

Resin and Asphaltene Gravimetric Determination A 100 mL quantity of n-pentane is added to a preweighed sample of approximately 5 g of oil. The flask is shaken well and allowed to stand for 30 minutes.27 The sample is filtered through a 0.45 mm membrane using a minimum of rinsings of n-pentane. The precipitate is allowed to dry, then weighed. The weight of the precipitate as a fraction of the initial oil sample weight is reported as the percentage asphaltenes. The filtrate from the precipitation, the “maltene” fraction, is recovered and made up to 100 mL with n-pentane. A 15 g, a 1 cm diameter column of activated silica gel is prepared. The top of the column is protected by a 1 cm layer of sodium sulphate. A 5 mL aliquot of the maltene fraction is loaded onto the column. A 60 mL volume of 1:1 (v:v) benzene:hexane is eluted through the column and discarded. A 60 mL volume of methanol, followed by a 60 mL volume of dichloromethane, are eluted through the column and combined. The methanol/dichloromethane fractions are reduced by rotary evaporation and blown down to dryness under nitrogen. The mass fraction of this dried eluent, compensating for the volume fraction used, is reported as the percentage of resins in the sample. Resin and Asphaltene Thin-Layer Chromatography Determination While no standard method for this technique exists, it has the advantages over the gravimetric methods of being much faster, requiring much less oil or product and being more reproducible. It has the disadvantage of requiring a sophisticated instrument, a TLC with a flame ionization detector (FID). A TLC that quantifies analytes developed on silica gel-coated glass rods, such as the Iatroscan Mark 6, is necessary for this method. Briefly, an aliquot of sample dissolved in dichloromethane at a concentration of 1 mg/mL is spotted at a point, the origin, near one end of a rod, the foot of the rod. The rods are then developed by immersion of the feet into a series of solvents to separate the four hydrocarbon groups. The origin points must remain above the liquid surface, but the feet of the rods must be immersed sufficiently to cause solvent to travel up the rods by capillary action.

Chapter | 4

Measurement of Oil Physical Properties

75

The first solvent used is n-hexane to develop the saturates. Toluene develops the aromatics. Finally, a 95% dichloromethane, 5% methanol mixture is used to develop the resins. The asphaltenes remain at the spotting origin. The hydrocarbon groups that are not quantified by this method, the saturates and aromatics, are removed by pyrolysis. A known standard is then applied to the chromarod and then quantified using an FID and an internal standard. A sample of 1 octadecanol at 1 mg/mL concentration is a convenient internal standard. This is spotted on the rod just prior to measurement, on the part of the rod pyrolyzed to remove the saturate and aromatic fractions. The development of the chemicals on the rods critically depends on the conditions. The rods must be developed in tanks to control the vapors in atmosphere. Also, temperature and humidity must remain as consistent as possible in order to achieve reproducible results. When drying after each development, the rods must rest in a controlled humidity chamber. Resin and asphaltene contents are determined as follows: %Resin ¼ CIS VIS AR =AIS

(8)

%Asphaltene ¼ CIS VIS AA =AIS

(9)

where: CIS: Internal standard concentration VIS: Internal standard volume AIS: Internal standard area from TLC integration AR: Resin area from TLC integration AA: Asphaltene area from TLC integration Note that while saturate and aromatic fractions are separated by the development process and could, in principle, be measured by TLC-FID, the drying process between development stages requires significant evaporation. This level of evaporation is significant enough to remove most of the volatile components, which includes a large fraction of both saturates and aromatics (but not the resins or asphaltenes). For this reason, this TLC-FID method is not suitable for saturate or aromatic determination.

Saturate and Aromatic Chromatographic Determination This method is adapted and simplified from a previously published method for crude oil and petroleum product determination.28 An 80 mg/mL solution of oil is prepared in hexane. A 3.0 g column of activated silica-gel is prepared, topped with 0.5 cm anhydrous sodium sulphate. The column is conditioned with 20 mL of hexane. An amount of 200 mL of the oil solution, approximately 16 mg of oil, is quantitatively transferred onto the column using an additional 3 mL of hexane to complete the transfer. The eluent is also discarded. Just prior to exposure of

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Oil Analysis and Remote Sensing

the sodium sulphate to the air, 12 mL of hexane is added to the column. The eluent is labeled fraction “F1.” F1 is considered to contain all the saturates, including the waxy components in the oil. The column is then eluted with 15 mL of 1:1 (volume:volume) benzene/ hexane or dichloromethane/hexane. The eluent is collected and labeled fraction “F2.” F2 is considered to contain the aromatic compounds in the oil, including the BTEX compounds, other alkylated benzene species, PAHs, and the alkylated PAH homologues. Half of fractions F1 and F2 are combined. This composite fraction is labeled “F3.” This fraction is used for analysis of total petroleum hydrocarbons (TPH). All the three fractions are concentrated under dry nitrogen. The fractions are then spiked with the internal standard, 100 mL of 200 ppm 5-a-androstane, and made up with hexane to 1 mL. The analysis for total petroleum hydrocarbons and saturates is performed by high-resolution capillary GC/FID using the following conditions: Column: Carrier Gas: Injection volume: Injector temperature: Detector temperature: Oven program:

30 m 0.32 mm ID HP DB5-HT fused silica column (0.10 mm film thickness); Helium, 3.0 mL/min, constant flow; 1.0 mL; 290 C; 325 C; 40 C for 2 minutes, followed by 25 C/minute to a final temperature of 340 C, then held for 15 minutes. The total run time is 29 minutes.

To calculate the concentration of hydrocarbons in each fraction, the area response attributed to the petroleum hydrocarbons must be determined. This area includes all of the resolved peaks and unresolved “hump.” This total area must be adjusted to remove the area response of the internal standards and GC column bleed. Column bleed is the reproducible baseline shift that occurs during the oven cycle of the GC. To determine this area, a hexane blank injection is analyzed before and after every 10 samples to determine the baseline response. The integration baseline is then set at a stable reproducible point just before the solvent peak. This baseline area for the blank run is subtracted from the actual sample run. The total areas of the chromatograms of F1, F2, and F3 are obtained by integration of all peaks, corrected by removal of the baseline. The area response attributable to the internal standard is calculated. The F3 fraction is used to calculate the TPH values for the oil.28 The F1 and F2 fractions are used to calculate the total saturate (TSH) and total aromatic (TAH) contents. Note that TPH should be within 10% of TSH þ TAH.

Chapter | 4

Measurement of Oil Physical Properties

77

As not all the oil is passed through the GC column, a simple sum of TSH, TAH, resin, and asphaltene contents will not equal 100%. This missing portion of the oil, which does not precipitate or get analyzed by the GC method, is approximated by proportionally dividing it into the saturate and aromatic portions. Thus the saturate content of the oil is commuted using: % Saturates ¼ TSH=ðTSH þ TAHÞð1 % Asphaltenes % ResinsÞ (10) Likewise, the aromatic content is computed using: % Aromatic ¼ TAH=ðTSH þ TAHÞð1 % Asphaltenes % ResinsÞ (11) Note that the asphaltene and resin contents may be determined by either gravimetric or TLC-FID method described earlier. For crude oils or products with high water content, it is necessary to dry the sample prior to the gravimetric determination of the hydrocarbon group contents. If a Karl-Fischer water content determination can be made, then the composition of the original product can be reported and adjusted for the observed water content. If not, the values should be reported as for dried product only.

4.4. QUALITY ASSURANCE AND CONTROL Most of the physical property methods described here rely on a single instrument and involve a simple measurement with little sample manipulation.28 For these methods, the instruments are calibrated as directed by the manufacturer or the appropriate ASTM method with chemical and/or gravimetric standards as appropriate. In addition, instrumental and operator performance should be monitored by periodic measurement of check standards. A control chart should be kept for each procedure, for the check or performance standard measurements. The check standard measurements are monitored closely. Failure of the check standard measurement to fall within the smaller of either a historical 95% confidence limit or the appropriate ASTM required repeatability should result in an investigation of the procedure. This typically includes required instrument maintenance, cleaning, recalibration, and measurement of the check standard until the desired precision and accuracy is reached. The chromatographic methods described here, including the dispersability tests and the hydrocarbon group analysis, involves significant sample preparation, followed by a measurement by gas chromatography. Such techniques require a higher level of effort to maintain quality assurance. Check or surrogate samples of either pure materials or certified reference standards should be processed in the same manner as the samples. Calibration should be accomplished with a second, separate set of certified reference materials. Internal standards should also be certified reference materials from reputable suppliers. Surrogate recovery, calibration stability, and internal standard response control charts should all be checked regularly to ensure procedure

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and measurement accuracy. Chromatograms should be checked to ensure that chromatographic quality, including good peak shape, baseline drift, column bleed, sample carryover, and chromatographic resolution are within acceptable limits.

4.5. EFFECTS OF EVAPORATIVE WEATHERING ON OIL BULK PROPERTIES Long experience has shown that the physical characteristics and chemical fingerprint of a crude oil can change greatly over the course of a spill incident. These changes have a profound effect on the fate, behavior, and effects of an oil in the environment. The oil may transmute to other states, evaporating, dissolving in water, or condensing to a semisolid residue, each new state having unique behaviors and eventual fates. In order to aid in the estimation and prediction of spill behavior, it is useful to know not only the characteristics of the fresh crude oil, but also those of oils at different stages of “weathering” in the environment. Previous work has shown that immediately after a spill, the dominant process of oil weathering is evaporation. The following discussion focuses on the effects of evaporative weathering on changes of oil physical properties and chemical compositions.

4.5.1. Weathering When oil is spilled, on either water or land, a number of transformation processes operate on the oil. In general, there are two types of transformation processes: the first is weathering, and the second is a group of processes (including spreading, movement of oil slicks, and sinking and ove-washing) related to the movement of oil in the environment. Weathering and movement processes overlap, with weathering strongly influencing how oil moves in the environment and vice versa. These processes depend very much on the type of oil spilled and the weather conditions during and after the spill. Thoroughly understanding the behavior of spilled oil in the environment is extremely important for development of oil spill models. Today’s sophisticated spill models combine the latest information on oil fate and behavior with computer technology to predict where the oil will go, what state it will be in, and when it gets there. “Weathering” is the term referring to a combination of a wide variety physical, chemical, and biological processes of a spilled oil in the environment. The weathering processes include evaporation, emulsification, natural dispersion, dissolution, microbial degradation, photo-oxidation, and other processes such as sedimentation, and oil-suspended particle interactions. Weathering has a very significant effect on most bulk oil properties. Unlike the chemical compositions, however, where environmental parameters only affect the rate and type of weathering, bulk properties of the oil are also highly variable depending on the physical conditions. The most important of these is

Chapter | 4

Measurement of Oil Physical Properties

79

temperature, but other factors such as pressure and the materials with which the oil is in contact also play a role. As an oil loses mass and changes in composition, several general trends in physical property changes can be observed: l

l

l

Density increases approximately linearly with increasing weathering. Density decreases approximately linearly with temperature. Viscosity increases with increasing weathering, but a simple functional relationship is not easy to develop. Viscosity increases approximately exponentially with decreasing temperature. Surface and interfacial tensions tend to increase slightly with increasing weathering.

4.5.2. Preparing Evaporated (Weathered) Samples of Oils A common technique for simulating weathering in the laboratory is evaporation. While this is only one of the possible processes in the natural environment, it is probably the dominant one for most spills, particularly in the first few hours or days following a spill. A laboratory oil-weathering technique by rotary evaporation allows for convenient preparation of artificially weathered oils with varying degrees of weight loss. A typical oil-weathering system consists of a rotary evaporator. The bath temperature of the evaporator should be variable from 20 C to 100 C 0.5 C. The rotation speed should be continuously variable from 10 to 135 rpm. The following evaporation procedure is used to evaporate oils: (1) The water bath is brought to a temperature of 80 C. (2) The empty rotary flask is weighed, and no more than one-third the volume of the rotary flask in oil is added and the flask reweighed. (3) The flask is mounted on the apparatus and the flask partially immersed in the water bath and spun at high speed, at least 120 rpm. A constant flow of air through the apparatus should be maintained by a vacuum pump. (4) At set intervals, the sample flask is removed and weighed. It is convenient to prepare two to three weathered samples for each type of oil measured. With a moderate flow rate through the instrument, a duration of 48 hours evaporation will come close, within 5 to 10%, to simulating the eventual final state of an oil in the environment. Intermediate fractions of approximately one- and two-thirds of the 48-hour loss by weight will simulate approximately the condition of the oil after a few hours to days and a few days to weeks of natural evaporation. The exact time taken to prepare these intermediate fractions is determined by estimation from the measured fractional mass-loss as a function of time for the 48-hour sample. The fraction mass-loss is calculated as: % weathering ¼ ðmi mf Þ=ðmi me Þ x 100%

(12)

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PART | III

Oil Analysis and Remote Sensing

where % weathering is the percentage evaporative mass-loss over the 48-hour period, mi is the initial mass of the flask and oil, mf is the final mass of the flask and oil, and me is the mass of the empty flask. A graph of % weathering as a function of time is plotted using the interval weighing data. The times for one-third (t1/3) and two-thirds (t2/3) of the 48-hour mass loss are interpolated from a time-weathering graph. Typical times for t1/3 range from 30 minutes to 2 hours, for t2/3, 8 to 12 hours. This technique allows for precise control of the evaporative weight loss for a target oil and can be directly correlated to bulk property and compositional changes of the weathered oil. By tracking weight loss as a function of time, an equation for predicting evaporation can be found. Also, from this same graph, it is possible to determine a point at which the evaporation rate is sufficiently slow that the oil may be considered to have achieved the maximum evaporative loss likely to be observed under the conditions of a marine spill. 0.94

0.94

Cook Inlet 2003 vs.T

Density (g/mL)

0.92

Cook Inlet 2003 0.92

0.90

25.0%

0.88

11.4%

0.86 Fresh 0.84 0.82

vs.W(%)

34.4%

Density (g/mL)

(a)

0.90 0.88 0.86

5 °C 15 °C

0.84 30 °C 0.82

0

5

10

15

20

25

30

0.80

35

0

10

Temperature (°C)

(b) 1.02

40

Platform Elly vs.W(%) 1.00

0.98

13.3% 7.9% 4.6%

0.96

Density (g/mL)

1.00

Density (g/mL)

30

1.02

Platform Elly vs.T

0.98 5 °C 0.96

Fresh 0.94

20

Weathering (%)

0

5

10

15

20

25

Temperature (°C)

30

35

15 °C 30 °C

0.94

0

2

4

6

8

10

12

14

Weathering (%)

FIGURE 4.1 Density versus temperature and weathering for a light (Cook Inlet) (a) and heavy (Platform Elly) (b) crude oil.

Chapter | 4

81

Measurement of Oil Physical Properties

4.5.3. Quantifying Equation(s) for Predicting Evaporation The evaporation kinetics are determined for each oil by measuring the weight loss over time from a shallow dish.30,31 Approximately 20 g of oil is weighed into a 139 mm petri dish. The oil weight is recorded by an electronic balance accurate to 0.01 g at set intervals and collected on a computer logging system. Measurements are conducted in a climate-controlled chamber at 15 C. Temperatures are monitored by a digital thermometer. The evaporation period can last from a few days for light oils to weeks for heavier products. The time versus weight-loss data series are fitted to a set of simple equations. The best curve-fit is chosen as the equation for predicting evaporation.

Effects of Evaporative Weathering on Crude Oil Density Densities of oils typically increase approximately 5 to 10% as oil weathers. Cook Inlet, a light oil, changes from 0.84 g/mL to 0.91 g/mL at 30 C (see 10000

10000

Cook Inlet 2003 vs.W(%)

1000

Viscosity (mPas)

Viscosity (mPas)

Cook Inlet 2003 vs.T

100 34.4% 25.0% 10

1000

100

10

11.4% Fresh

1

0

5

10

15

20

25

30

5 °C 15 °C 30 °C

1

35

0

10

1e+7

1e+5 13.3% 1e+4

7.9% 4.6%

1e+3

Fresh

0

5

10

15

20

25

Temperature (°C)

30

35

1e+6

Viscosity (mPas)

Viscosity (mPas)

30

40

1e+7

Platform Elly vs.T

1e+6

1e+2

20

Weathering (%)

Temperature (°C)

Platform Elly vs.W(%)

1e+5

1e+4

1e+3

1e+2

5 °C 15 °C 30 °C

0

2

4

6

8

10

12

14

Weathering (%)

FIGURE 4.2 Viscosity versus temperature and weathering for light (Cook Inlet) and heavy (Platform Elly) crude oils.

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Oil Analysis and Remote Sensing

Figure 4.1a), while Platform Elly, a very heavy crude oil, has a fresh density of 0.9531 g/mL and increases to 0.9843 g/mL in its most weathered state at 30 C (Figure 4.1b). From Figure 4.1, it can be seen that, to a first approximation,

30

34.4%

29

25.0%

28 27 26

Fresh

25 0

5

10

15

20

25

30

34 o/w

vs.T

32 30 28 26

34.4% 25.0%

24 22

11.4% Fresh 0

5

10

15

20

25

30

35

34

vs.T

o/b

32 30 28 26 24

34.4% 25.0% 11.4%

22

Fresh

20

0

5

10

15

20

25

Temperature (°C)

30

35

31

Cook Inlet 2003 vs.W(%) o/a

5 °C 15 °C

30

30 °C

29 28 27 26 25 24

35

Interfacial Tension (oil/water) (mN/m)

Interfacial Tension (oil/water) (mN/m)

11.4%

Surface Tension (oil/air) (mN/m)

31

24

Interfacial Tension (oil/3.3%brine) (mN/m)

32

Cook Inlet 2003 vs. T o/a

Interfacial Tension (oil/3.3%brine) (mN/m)

Surface Tension (oil/air) (mN/m)

32

30 °C 15 °C 5 °C 0

10

20

30

40

34

vs.W(%)

o/a

32 30 28 26

5 °C 15 °C

24 30 °C 22

0

10

20

30

40

34

vs.W(%)

32

o/b

30 28 26 24

5 °C 15 °C

22 30 °C 20

0

10

20

30

40

Weathering (%)

FIGURE 4.3 Surface and interfacial tensions as a function of temperature and weathering for Cook Inlet (2003).

Chapter | 4

Measurement of Oil Physical Properties

83

density increases linearly with increasing mass-loss and decreasing temperature. Better extrapolations can be made from log-log extrapolations of both quantities. Note that the uncertainties in density are very small: 0.0002 g/mLdapproximately 1 part in 5,000.

Effects of Evaporative Weathering on Crude Oil Viscosity In contrast to most other physical properties, the viscosity of an oil can change by orders of magnitude with weathering and changes in temperature. For example, the viscosity of Cook Inlet (2003) changes from 5.8 mPa s to 67.0 mPa s at 30 C (see Figure 4.2), while fresh Platform Elly has a viscosity of 1070 mPa s, and reaches 52280 mPa s in the most weathered fraction (Figure 4.3). As can be seen from the logarithm of viscosity is roughly inversely linear with temperature, but the effects of weathering on viscosity are more complex. Uncertainties in viscosity are 5%. Effects of Evaporative Weathering on Crude Oil Surface and Interfacial Tensions Surface and interfacial tensions have no simple quantitative relationships in general to either the degree of weathering or the temperature. Surface tensions however, do not vary greatly from oil to oil; values from 25 mN/m to 32 mN/m are typical for almost all types of oil. Interfacial tensions for oil/water and oil/ 3.3% brine are often marginally lower than the corresponding oil/air surface tension. Oil/brine interfacial tensions are usually somewhat higher than the corresponding oil/(pure) water values. Typical values for both range from 18 mN/m to 32 mN/m. Surface and interfacial tensions tend to decrease with temperature and increase with weathering. Care should be taken not to overinterpret the significance of surface and interfacial tension values; however, the errors on these measurements are relatively large, 15%, and the relative variations of the values are fairly small.

REFERENCES 1. ASTM D 5002. Standard Test Method for Density and Relative Density of Crude Oils by Digital Density Analyzer. Conshohocken, PA: American Society for Testing and Materials (ASTM); 2009. 2. API 82. American Petroleum Institute (API), Petroleum Measurement TablesdVolume XI/XII. West Conshohocken, PA: American Society for Testing and Materials; 1982. 3. Jokuty P, Fingas M, Whiticar S. Oil Analytical Techniques for Environmental Purposes. AMOP 1994;245. 4. ASTM D 971. Standard Test Method for Interfacial Tension of Oil Against Water by the Ring Method. West Conshohocken, PA: American Society for Testing and Materials; 2009. 5. Jokuty P, Fingas M, Whiticar S, Fieldhouse B. A Study of Viscosity and Interfacial Tension of Oils and Emulsions, Manuscript Report EE-153, Ottawa, ON: Environment Canada, 1995.

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6. Song B, Springer J. Determination of Interfacial Tension from the Profile of a Pendant Drop Using Computer-aided Image Processing. Colloid Interface Sci. 1996;64. 7. ASTM D1310. Standard Test Method for Flash Point and Fire Point of Liquids by Tag Open-Cup Apparatus. West Conshohocken, PA: American Society for Testing and Materials; 2007. 8. ASTM D 6450. , Standard Test Method for Flash Point by Continuously Tester. West Conshohocken, PA: American Society for Testing and Materials; 2009. 9. ASTM D 93. American Society for Testing and Materials (ASTM), Standard Test Method for Flash Point by Pensky-Martens Closed Cup Tester. West Conshohocken, PA: American Society for Testing and Materials; 2009. 10. ASTM D 7094. Standard Test Method for Flash Point by Modified Continuously Closed Tester. West Conshohocken, PA: American Society for Testing and Materials; 2009. 11. Montemayor RG, Rogerson JE, Colbert JC, Schiller SB. Reference Verification Fluids for Flash Point Determination. J. Test. Eval. 1999;27. 12. ASTM D 97. Standard Test Method for Pour Point of Petroleum Oils. West Conshohocken, PA: American Society for Testing and Materials; 2009. 13. ASTM D 4294. Standard Test Method for Sulfur in Petroleum Products by Energy-Dispersive X-ray Fluorescence Spectroscopy. West Conshohocken, PA: American Society for Testing and Materials; 2009. 14. ASTM D 4377. Standard Test Method for Water in Crude Oils by Potentiometric Karl Fischer Titration. West Conshohocken, PA: American Society for Testing and Materials; 2009. 15. Fingas M, Fieldhouse B, Mullin J. Studies of Water-in-oil Emulsions: Stability and Oil Properties. AMOP 1998;1. 16. Fingas M, Fieldhouse B. Studies on Crude Oil and Petroleum Product Emulsions: Water Resolution and Rheology. Colloids Surf. A. 2009;67. 17. ASTM F 2059. Standard Test Method for Laboratory Oil Spill Dispersant Effectiveness Using the Swirling Flask. West Conshohocken, PA: American Society for Testing and Materials; 2007. 18. Jokuty P, Whiticar S, McRoberts K, Mullin J. Oil Adhesion TestingdRecent Results. AMOP 1996;9. 19. ASTM D 5. Standard Test Method for Penetration of Bituminous Materials. West Conshohocken, PA: American Society for Testing and Materials; 2009. 20. ASTM D 6560. Standard Test Method for Determination of Asphaltenes (Heptane Insolubles) in Crude Petroleum and Petroleum Products. West Conshohocken, PA: American Society for Testing and Materials; 2006. 21. ASTM D 2006. Method of Test for Characteristic Groups in Rubber Extender and Processing Oils by the Precipitation Method (Withdrawn 1975). West Conshohocken, PA: American Society for Testing and Materials; 1965. 22. ASTM D 2007. American Society for Testing and Materials (ASTM), Standard Test Method for Characteristic Groups in Rubber Extender and Processing Oils and Other PetroleumDerived Oils by Clay-Gel Absorption Chromatographic Method. West Conshohocken, PA: American Society for Testing and Materials; 2007. 23. ASTM D 4124. Standard Test Methods for Separation of Asphalt into Four Fractions. West Conshohocken, PA: American Society for Testing and Materials; 2006. 24. Barman BN. Hydrocarbon-Type Analysis of Base Oils and Other Heavy Distillates by ThinLayer Chromatography with Flame-Ionization Detection and by the Clay-Gel Method. J. Chromat. Sci. 1996;219. 25. Speight JG. The Chemistry and Technology of Petroleum. New York: Marcel Dekker; 2007. 26. Becker JR. Chapter 13, Asphaltene Test Methods, Crude Oil Waxes, Emulsions and Asphaltenes. Tulsa, OK: Penn Well Publishing Co; 1991.

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27. Hollebone B, Wang Z, Landriault M, Smith P. A New Method for the Determination of the Hydrocarbon Groups in Oils: Saturates, Aromatics, Resins, and Asphaltenes (SARA). AMOP 2003;31. 28. Wang ZD, Fingas M, Li K. Fractionation of ASMB Oil, Identification and Quantitation of Aliphatic Aromatic and Biomarker Compounds by GC/FID and GC/MSD (Parts I and II). J. Chromat. Sci. 1994;361. 29. Environment Canada, Oil Properties Database, http://www.etc-cte.ec.gc.ca/databases/ OilProperties/oil_prop_e.html, accessed May 2010. 30. Fingas M. The Evaporation of Oil Spills. AMOP 1995;43. 31. Fingas M. Modeling Evaporation Using Models That Are Not Boundary-Layer Regulated. J. Haz. Mat. 2004;27.

APPENDIX 4.1 Table A4.1 gives the environmentally relevant properties of selected crude oils. TABLE A4.1 Environmentally-Relevant Properties of Selected Crude Oils29 Alaska North Slope Prudhoe Bay, Alaska, USA

Saudi Arabia

Mississippi Brent Canyon Blend Federated Block 807 Gulf of North Mexico, Sea, United Alberta, Louisiana, USA Kingdom Canada

Arabian Light

West Texas Intermediate

Texas USA

0 C

0.8777

0.8776

0.8472

0.8413

0.9310

0.8594

15 C

0.8663

0.8641

0.8351

0.8293

0.9461

0.8474

30.89

31.30

37.8

38.9

17.5

34.38

0 C

23.2

32.6

16

10

88.1

19.2

11.5

13

6

4

4.8

8.6

mN/m 0 C

27.3

27.2

28.0

27.3

28.8

27.4

26.4

26

25.5

25.8

28.2

26.0

mN/m 0 C

26.7

23.5

25.7

18.7

24.4

19.3

23.6

23.8

22.7

15.9

24.1

15.8

Oil-sea water Interfacial tension

mN/m 0 C

22.5

21.3

24.9

17.6

26.0

18.8

20.2

21.6

22.5

16.2

26.6

15.6

Flashpoint

C

<8

<10

<30

15

<0

<10

Pour Point

C

32

21

42

57

22

Density

g/mL

API Gravity Viscosity

mPas

15 C Surface Tension Oil-water Interfacial tension

15 C

15 C

15 C

(Continued )

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Oil Analysis and Remote Sensing

TABLE A4.1 Environmentally-Relevant Properties of Selected Crude Oils29dcont’d Alaska North Slope Prudhoe Bay, Alaska, USA Emulsion Formation

Arabian Light

Saudi Arabia

Visual Unstable Meso Stability Complex 92.7 Modulus (Pa) 91.1 Emulsion Water Content

Mississippi Brent Canyon Blend Federated Block 807 Gulf of North Mexico, Sea, United Alberta, Louisiana, USA Kingdom Canada NM

NM

Meso

West Texas Intermediate

Texas USA Unstable

26 79.7

Water

% (w/w)

<0.1

<0.1

0.4

NM

11.9

<0.1

Sulphur

% (w/w)

1.11

1.93

0.39

0 29

0.83

0.86

Saturate

% (w/w)

75.0

75.5

72

74

73.3

78.5

Aromatic

% (w/w)

15.0

15.2

23

21

15.45

14.8

Resin

% (w/w)

6.1

5.7

4

3

6.22

6.0

Asphaltene

% (w/w)

4.0

3.6

1

1

5.03

0.7

Wax

% (w/w)

2.6

2.7

7

0.6

2.8

Adhesion

g/m2

20

17

12

2

NM

12

47

19

45

61

49.6

28

Chemical % (Corexit Dispersability 9500)

Chapter 5

Introduction to Oil Chemical Analysis Merv Fingas

Chapter Outline 5.1. Introduction 87 5.2. Sampling and Laboratory 87 Analysis 5.3. Chromatography 89

5.4. Identification and Forensic 96 Analysis 5.5. Field Analysis 107

5.1. INTRODUCTION An important part of the field of oil spill control is the analysis of oil in various media. Oil analytical techniques are a necessary part of the scientific, environmental, and engineering aspects of oil spills.1-4 Analytical techniques are used extensively in environmental assessments of fate and effects. Laboratory analysis can provide information to help identify an oil if its source is unknown or what its sources might be. With a sample of the source oil, the degree of weathering and the amount of evaporation or biodegradation can be determined for the spilled oil. Through laboratory analysis, the more toxic compounds in the oil can be measured, and the relative composition of the oil at various stages of the spill can be determined. This is valuable information to have as the spill progresses. In nonspill situations, analytical techniques are used extensively to measure the oil content of soil and water for environmental quality purposes. Many jurisdictions have standards on the petroleum content of waters and soils for various uses. In addition, many laws exist for the maximum oil content in soils. Soils must often be removed for treatment before lands can be transferred from one owner to another.

5.2. SAMPLING AND LABORATORY ANALYSIS Taking a sample of oil and then transporting it to a laboratory for subsequent analysis is common practice. While there are many procedures for taking oil Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10005-X Copyright Ó 2011 Elsevier Inc. All rights reserved.

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samples, it is always important to ensure that the oil is not tainted from contact with other materials and that the sample bottles are precleaned with solvents, such as hexane, that are suitable for the oil.5 The simplest and most common form of analysis is to measure how much oil is in a water, soil, or sediment sample.6 Such analysis results in a value known as total petroleum hydrocarbons (TPH). The TPH measurement can be obtained in many ways, including extracting the soil, or evaporating a solvent such as hexane and measuring the weight of the residue that is presumed to be oil. There now exist certified laboratories that use certified petroleum hydrocarbon measurement techniques.6 These should be used for all studies. One of the most serious difficulties in older studies occurred when inexperienced staff tried to conduct chemical procedures. Analytical methods are complex and cannot be conducted correctly without chemists familiar with the exact procedures. Furthermore, field instrumentation requires calibration using standard procedures and field samples during the actual test. These samples must be taken and handled by standard procedures. Certified standards must be used throughout to ensure good Quality Assurance/Quality Control (QA/QC) procedures. In this era, it is simply unacceptable not to use certified methods, laboratories, and chemists.

5.2.1. Incorrect and Obsolete Methods Several attempts to perform oil analysis have been made using methods that are not scientifically valid. One of these is the use of colorimetry. This method has never been scientifically valid for oil measurement as oil does not have what is known as a color center, that is, a molecular absorption center for a specific band of light.7-9 As oil is a mixture of hundreds of compounds, there is obviously not a single light-absorbing centre. This method results in oil measurements that are typically 100% incorrect. Another series of methods involved extracting oil from soil or water using fluorinated or chlorinated hydrocarbons. Since these extractants were ozonedepleting, they were removed from the market over 20 years ago. The extracted hydrocarbons were then “measured” using infrared light, as hydrocarbons in such solvents do absorb at specific wavelengths. The method was the standard oil in soil technique in several countries and yielded repeatable results. The ozone-depleting substances were replaced with hexane. Hexane is not a good extractant of oil from soil, and thus these methods are not as popular as the older methods were. The use of hexane also led to renaming the results of these methods from Total Petroleum Hydrocarbons (TPH) to hexaneextractables.10 There are several “meters” sold on the market that offer oil readings; however, none of these are reliable or accurate.8

Chapter | 5

Introduction to Oil Chemical Analysis

89

Fluorometry is a technique sometimes used for measuring or estimating concentrations of oil in water. A fluorometer uses UV or near UV to activate aromatic species in the oil.11 The UV activation energy is more sensitive to the naphthalenes and phenanthrenes, whereas the near UV is more sensitive to large species such as fluorenes. The composition of the oil changes with respect to aromatic content as it weathers and is dispersed in water, with the concentration of aromatics increasing. Thus, the apparent fluorescent quantity increases in this process. It must be noted that fluorometers cannot truly be calibrated for the oil as there are many variables, as explained above. The errors encountered all increase the apparent value of the oil concentration in the water column. Incorrect calibration procedures can distort concentration values up to 10 times their actual value, or even more. Correct analytical methods involve performing accurate gas chromatograph (GC) measurements both in the laboratory and in the field.

5.3. CHROMATOGRAPHY The primary method for oil analysis, as well as for many chemicals in the environment, is gas chromatography; this method will be described in the subsequent section. One should note that other chromatography methods and other analytical methods are sometimes used for oils. These include liquid chromatography, sometimes used for PAH analysis and inductively coupled plasma (ICP) instruments for measuring metals in oils. These and many other techniques are not described in this section.

5.3.1. Introduction to Gas Chromatography The standard method for oil analysis is to use a GC.2,3,12,13,14 A small sample of the oil extract (typically measured in microliters, mL), often in hexane, and a carrier gas, usually helium or hydrogen, are passed through a capillary column. The sample is injected into a heated chamber from where its vapors pass into the silica column. The silica column is coated with absorbing materials, and, because the various components of the oil have varying rates of adhesion, the oil separates because these components are absorbed at different rates onto the column walls. The gases then pass through a sensitive detector. The injector, column, and detector are often maintained at constant temperatures to ensure repeatability. The system is calibrated by passing known amounts of standard materials through the unit. The amount of many individual components in the oil is thereby measured. The components that pass through the detector can also be totaled, and a TPH value determined. It is important to note that only the vapors pass into the column initially. Heavier contaminants can foul the injector and first part of the column. It is therefore important that the sample be subjected to a cleanup procedure before it is injected into the column. Cleanup procedures can be complex and involve several steps.

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While a GC measurement is highly accurate, this measurement does not include resins, asphaltenes, and some other components of the oil with higher molecular weight that do not vaporize and pass through the column. These heavier components can be determined separately using open column chromatography or precipitation techniques. The detectors used in chromatography are important. An important detector for petroleum hydrocarbons is the flame ionization detector (FID).2,3 The principle behind this instrument is simple as most compounds show variable ion conductivity burned in a hydrogen flame. This detector is simple and has the advantage of yielding relatively similar signals for different hydrocarbons, thus making calibration and quantification simple. Another detector commonly used for oils is the mass spectrometric detector. The analytes from the GC column are introduced into a vacuum chamber and ionized, and then these ions are separated according to mass and passed to a detector. The detected signals are then analyzed by a computer and output to the user as peaks of given mass or even possible compound identification. The mass spectrometer provides information about the structure of the substance so that each peak in the chromatogram can be more positively identified. The methods are abbreviated as GC-FID, if a gas chromatograph and flame ionization detector are used or GC-MS, if a gas chromatograph and a mass spectrometer detector are used. A typical GC-FID chromatogram of a light crude oil with some of the more prominent components of the oil identified is shown in Figure 5.1.2,3 This chromatogram shows some of the many features of oil that are identified by this analytical technique. The bulk of crude oils, especially light ones, have a large proportion of n-alkanes, as can be seen by the large peaks that constitute a large portion of this chromatogram. This is a light crude oil that can be evidenced by the fact that the highest peak is C15. A more weathered crude might have its highest peak at C18 or more. The top of the chromatogram is typically shaped as a curve, peaking to C15, as it is here. Under the peaks is a hump, often called the unresolved complex mixture, or UCM. This is an aggregate of largely unresolved peaks of alkane origin. At C16, for example, there are already thousands of isomers that cannot be resolved by typical GC methods. Between the n-alkane peaks are smaller peaks, most of which are aromatic compounds. Two standard biomarkers (here, isoprenoids and branched alkanes) are usually evident in such a chromatogram, Pristane (near nC17) and Phytane (near nC18). These have been used to assess state of weathering; however, other compounds are now typically used. Figure 5.2 shows the GC-FID chromatograms of 10 oils. Figure 5.2A to C shows the chromatograms of three lighter crude oils: Arabian medium crude oil, Hedrun crude oil, and Gullfaks,2,3 Figure 5.2D shows a chromatogram of Orimulsion, a bitumen. In this chromatogram one notes that almost all n-alkanes are not present, and the chromatogram largely consists of the

91

C28

C26

C24

C22

C20

Introduction to Oil Chemical Analysis

C21

Chapter | 5

Chromatographic retention time FIGURE 5.1 GC-FID of a light crude oil. This chromatogram illustrates many features of the chromatograms of crude oils. The bulk of crude oils, especially light ones, have a large proportion of n-alkanes, as can be seen by the large peaks that constitute a large portion of this chromatogram.

unresolved complex mixtures, or UCM. Figure 5.2E shows the chromatogram of IFO-30, Intermediate fuel oil, which is a mixture of a diesel fraction and Bunker C. Figure 5.2F shows the chromatogram of Bunker C, and Figures 5.2G-J show jet fuel, diesel fuel, lubrication oil, and number 6 fuel oil, respectively. The mass spectrometer provides information about the structure of the substance so that each peak in the chromatogram can be positively identified. An important technique is that of SIM (selective ion monitoring), where one can monitor the ion most typically associated with the target compound. This technique enables the detection and quantification of many compounds in oil that otherwise would not be separately resolved.2,3 Figure 5.3 shows three chromatograms first by GC-FID and then by GC-MS using SIM. Figures 5.3A and B are chromatograms of a light Alberta crude oil; Figures 5.3C and D are chromatograms of California heavy crude oil, and Figures 5.3E and 5.3F are chromatograms of Orimulsion Bitumen. The two chromatograms (e.g., FID/ SIM) are quite different. The SIM chromatograms are no longer recognized as the types of oils as shown by FID. However, one can see that the peaks are more clearly defined and that the SIM can provide different and important information. The disadvantage of the SIM is that each peak must be quantified separately using an internal standard. Most fuels and oils show a typical distribution pattern in GC-FID. The idealized patterns can be seen in Figure 5.4, a figure that shows the alkane distribution. All graphs are n-alkanes except for the lube oils where the alkanes are highly branched. These bar graphs were created from

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Oil Analysis and Remote Sensing

FIGURE 5.2 GC-FIDs of several oils. Figure 5.2A shows the chromatogram of Arabian medium crude oil. Figure 5.2B is a chromatogram of Hedrun crude oil, a light oil. Figure 5.2C is a chromatogram of Gullfaks, another light oil. Figure 5.2D shows a chromatogram of Orimulsion, a bitumen. In this chromatogram one notes that almost all n-alkanes are not present, and the chromatogram largely consists of the unresolved complex mixtures or UCM. Figure 5.2E shows the chromatogram of IFO-39, Intermediate fuel oil, which is a mixture of a diesel fraction and Bunker C. One can see the peaks of diesel fuel, peaking at about C14 and that of Bunker C, peaking at about C28. In Figure 5.2F the chromatogram of Bunker C is shown. Figures 5.2G, 2H, 2I, and 2J show jet fuel B, diesel fuel, lubrication oil, and number 6 fuel oil, respectively.

Chapter | 5

Introduction to Oil Chemical Analysis

93

FIGURE 5.2 Continued

quantitative analysis and approximate the alkane chromatograms of the same oils.

5.3.2. Methodology Modern chromatographic methods require that the injected sample contents be of certain types and that they do not foul the injector or column. Thus, several cleanup methods have developed over the years.1,6 The basic methods involve extracting the oil using dichloromethane (DCM), sometimes in combination with other solvents such as hexane. This procedure will leave the DCM insoluble material, such as soil and wood, and remove the DCM soluble material, which is largely petroleum oil. Surrogate chemicals are often added at this stage; these substances are compounds, typically deuterated hydrocarbons, that are not present in oil and will serve to identify peaks in subsequent analyses. The DCM extract is often filtered and treated to

94

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Oil Analysis and Remote Sensing

FIGURE 5.3 The GC-FID and GC-MS with SIM at ion 85m/e. The left-hand columns are the GC-FID chromatograms, and the right-hand side are the GC-MS and SIM chromatograms. Figures A and B are chromatograms of a light Alberta crude oil; Figures C and D are chromatograms of California heavy crude oil, and Figures E and F are chromatograms of Orimulsion Bitumen. One notes that the two chromatograms (e.g., FID/SIM) are quite different. The SIM chromatograms no longer have the recognizability of the types of oils as shown by FID. However, one can see that the peaks are more clearly defined and that the SIM can provide separate information. The disadvantage of the SIM is that each peak must be quantified separately using a standard.

Chapter | 5

95

Introduction to Oil Chemical Analysis

Lube Oil

Gasoline

4 6

8 10 12 14 Carbon Number

15

20

25

30

35

40

Carbon Number

Jet Fuel

Typical Crude

6 8 10 12 14 16 18 Carbon Number 0

5

15 10 Carbon Number

20

25

30

35

40

35

40

Diesel Fuel Bunker C 6 8 10 12 14 16 18 20 22 24 Carbon Number

10

15

20

25 30 Carbon Number

FIGURE 5.4 The bar graph distribution of alkanes for typical oils and fuels. These graphs were generated from the quantitative analysis of several oils. It should be noted that the alkanes are typically n-alkanes for all oils except for lube oils, where they are highly branched alkanes.

remove water before injection into the GC. At each point in this cleanup, the sample is quantified to allow measurement of those groups of materials removed. These measurements then form the basis for various forms of TPH measurement. One such method as developed by Dr. Zhendi Wang of Environment Canada is shown in Figure 5.5.2,3 In the method illustrated in Figure 5.5, the sample is separated into aliphatic, aromatic, and polar fractions using an open silica column. Several tests of this have been carried out to ensure that separation is complete. Having these fractions separated ensures that subsequent chromatographic analysis is not affected by interferences between the three fractions. The peaks that are typically quantified for analysis and possibly for identification are listed in Table 5.1.2,3 As described later, many of the these peaks are useful when combined in ratios. Often these ratios are unique and can be used for positive identification of an oil. There are many published methods and standards for oil analysis; several of these are listed in Table 5.2.

96

PART | III

Oil Analysis and Remote Sensing

Weigh Sample add surrogates

serially extract sample with dichloromethane/hexane

filter and concentrate extract

gravimetrically determine TPH

silica column fractionation fraction 1 aliphatics

fraction 2

fraction 3

aromatics

mixed

50% DCM/Hexane hexane gravimetric saturates aromatics determinations 5-- -Androstane d14-Terphenyl internal standards Hopane

GC/MS SIM

GC/MS (SIM)

GC/FID PAHs n-alkane hopanes n-alkane quantification distribution & steranes

half F1&F2 TPH 5- -Androstane

fraction 4 polars Methanol polars polars

GC/FID

Benzenes PAH alkylated homologues

TPH

FIGURE 5.5 Illustration of the Dr. Wang analysis method developed for Environment Canada. After cleanup procedures, the sample is separated into aliphatic, aromatic, and polar fractions. This enables very clear chromatographic analysis without interference between these fractions. This method yields many analytical parameters.

5.4. IDENTIFICATION AND FORENSIC ANALYSIS The foregoing information can then be used to predict how long the oil has been in the environment and what percentage of it has evaporated or biodegraded.15-23 This is possible because some of the components in oils, particularly crude oils, are very resistant to biodegradation, whereas others are resistant to evaporation. This difference in the distribution of components then allows the degree of weathering of the oil to be measured. The same technique can be used to “fingerprint” an oil and positively identify its source. Certain compounds are consistently distributed in oil, regardless of weathering, and these are used to identify the specific type of oil. The effect of weathering is particularly important as it may negate the use of standard GC-FID to positively identify an oil.23, 28-31 Figure 5.6 shows the effect of weathering on the GC-FID chromatogram of a light crude oil. As the oil weathers, more and more of the lower n-alkanes are lost to evaporation, the most important component of weathering. Figure 5.6A shows the

Chapter | 5

97

Introduction to Oil Chemical Analysis

TABLE 5.1 Target Analytes/Compounds for Oil Spill Studies Analyte

Analyte

Target Ion

Aliphatic Hydrocarbons

BTEX and C3 Benzenes

n-C8

Benzene

78

n-C9

Toluene

91

n-C10

Ethylbenzene

105

n-C11

Xylenes

105

n-C12

C2 - benzenes

105

n-C13

C3 - benzenes

105

n-C14

PAHs

n-C15

Naphthalene

128

n-C16

C1 - naphthalene

142

n-C17

C2 - naphthalene

156

Pristane

C3 - naphthalene

170

n-C18

C4 - naphthalene

184

Phytane

Phenanthrene

178

n-C19

C1 - phenanthrene

192

n-C20

C2 - phenanthrene

206

n-C21

C3 - phenanthrene

220

n-C22

C4 - phenanthrene

234

n-C23

Fluorene

166

n-C24

C1 - fluorene

180

n-C25

C2 - fluorene

194

n-C26

C3 - fluorene

208

n-C27

Chrysene

228

n-C28

C1 - chrysene

242

n-C29

C2 - chrysene

256

n-C30

C3 - chrysene

270

n-C31

Biphenyl

154

(Continued )

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TABLE 5.1 Target Analytes/Compounds for Oil Spill Studiesdcont’d Analyte

Analyte

Target Ion

n-C32

Benzo[e]pyrene

252

n-C33

Benzo[a]pyrene

252

n-C34

Perylene

252

n-C35

Dibenzothiophene

184

n-C36

C1 - Dibenzothiophene

198

n-C37

C2 - Dibenzothiophene

212

n-C38

C3 - Dibenzothiophene

226

n-C39 n-C40 EPA Priority PAH pollutants Naphthalene

128

Phenanthrene

178

Fluorene

166

Chrysene

228

Acenaphthylene

152

Acenaphthene

153

Anthracene

178

Fluoranthene

202

Pyrene

202

Benz[a]anthracene

228

Benz[b]fluoranthene

252

Benzo[k]fluoranthene

252

Benzo(g,h,i)perylene

276

Biomarkers Triterpanes Tricyclic terpanes

191

Chapter | 5

99

Introduction to Oil Chemical Analysis

TABLE 5.1 Target Analytes/Compounds for Oil Spill Studiesdcont’d Analyte

Analyte

Target Ion

Tetracyclic terpanes

191

Pentacyclic terpanes

191

C23H42

191

C24H44

191

C27H46 (Ts) & (Tm)

191

C29H50&C30H52 ab-hopane

191

C30-35H52-62 22S/22R

191

Steranes C27 20 R/S-cholestanes

217,218

C28 20 R/S-ergostanes

217,218

C29 20 R/S-stigmastanes

217,218

unweathered oils with the Cn Benzenes and naphthalenes very obvious. After about a 30% loss of oil through evaporation, the Cn Benzenes are lost, and many of the lower alkanes are shown in Figure 5.6B. After 44.5% of the mass is evaporated, as shown in Figure 5.6C, even the naphthalenes are not clearly present. It would be very difficult to simply compare weathered and unweathered oils simply by comparing the chromatograms. Although there are some ways to partially compensate for this, modern technology uses other components of the oil to forensically identify oils.

5.4.1. Biomarkers Biological markers or biomarkers are an important hydrocarbon group in petroleum analysis.32-34 Biomarkers are complex molecules derived from formerly living organisms. Biomarkers found in crude oils, rocks, and sediments show little change in structures from their parent organic molecules, or so-called biogenic precursors (for example, hopanoids, and steroids), in living organisms. Biomarker concentrations are relatively low in oil, often in the range of several hundred ppm. Biomarkers are useful because they retain all or most of the original carbon skeleton of the original natural product; this structural similarity reveals more information about oil origins than other compounds. Petroleum geochemists have historically used biomarker

100

TABLE 5.2 List of Standards Applicable to Oil Measurement Standards Organization

Method Number

ASTM

Analyte

Description

Reference

D5739-06

Standard Practice for Oil Spill Source Identification by Gas Chromatography and Positive Ion Electron Impact Low Resolution Mass Spectrometry

GC-EI

GC pattern

Fingerprinting for oil identification

24

ASTM

D3328-06

Standard Test Methods for Comparison of Waterborne Petroleum Oils by Gas Chromatography

GC-EI

GC pattern

Fingerprinting for oil identification

24

ASTM

D3415-98

Standard Practice for Identification of Waterborne Oils

GC-FID

oil ID

Identification of oils on water

24

ASTM

D3326-07

Standard Practice for Preparation of Samples for Identification of Waterborne Oils

GC-FID

oil ID

Identification of oils on water

24

ASTM

D5739-00

Standard Practice for Oil Spill Source Identification by Gas Chromatography and Positive Ion Electron Impact Low Resolution Mass Spectrometry

GC-EI

GC pattern

Fingerprinting for oil identification

24

Oil Analysis and Remote Sensing

Technique

PART | III

Title

TABLE 5.2 List of Standards Applicable to Oil Measurementdcont’d Method Number

ASTM

Technique

Analyte

Description

Reference

F2059-06

Standard Test Method for Laboratory Oil Spill Dispersant Effectiveness Using the Swirling Flask

GC-FID

TPH

TPH for water, chemical dispersion quantification

24

ASTM

D5412-93

Standard Test Method for Quantification of Complex Polycyclic Aromatic Hydrocarbon Mixtures or Petroleum Oils in Water

GC-EI

PAHs

Quantification of PAHS

24

ASTM

D6352-04

Standard Test Method for Boiling Range Distribution of Petroleum Distillates in Boiling Range from 174 to 700 C by Gas Chromatography

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation on oil e extended range

24

ASTM

D2887-08

Standard Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation on oil

24

ASTM

D5307-97

Standard Test Method for Determination of Boiling Range Distribution of Crude Petroleum by Gas Chromatography

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation on crude oil

24

ASTM

D7169-05

Standard Test Method for Boiling Point Distribution of Samples with Residues Such as Crude Oils and Atmospheric

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation

24

101

(Continued )

Introduction to Oil Chemical Analysis

Title

Chapter | 5

Standards Organization

102

TABLE 5.2 List of Standards Applicable to Oil Measurementdcont’d Standards Organization

Method Number

Title

Technique

Analyte

Reference

Method to preserve oil samples

24

and Vacuum Residues by High Temperature Gas Chromatography D3325-90

Standard Practice for Preservation of Waterborne Oil Samples

Sample Preservation

API

PHC

Determination of Petroleum Hydrocarbons

GC-FID

PHC

Petroleum Hydrocarbons

25

API

GRO

Determination of Petroleum Hydrocarbons

GC-FID

GRO

Gasoline range organic compounds

25

API

DRO

Determination of Petroleum Hydrocarbons

GC-FID

DRO

Diesel range organic compounds

25

EPA

846

Gas Chromatographic (GC) Methods

GC-FID/EI

organics

General GC method

26

Oil Analysis and Remote Sensing

ASTM

PART | III

Description

TABLE 5.2 List of Standards Applicable to Oil Measurementdcont’d Method Number

EPA

Technique

Analyte

Description

Reference

610

Determination of priority PAHS in municipal and industrial wastes

GC-EI

PAHs

PAHs in wastes

26

EPA

1664

N-hexane Extractable Material (HEM; Oil and Grease) and Silica Gel Treated N-Hexane Extractable Material (SGT-HEM: Non-polar Material) by Extraction and Gravimetry

gravimetry

TPH

N-hexane extractables in various substrates

26

CCME

1397

Canada-Wide Standard for Petroleum Hydrcarbons (PHC) in Soil

GC-FID/EI

TPH

Suite of analysis techniques for hydrocarbons in soil

27

CCME

1399

Canada-Wide Standard for Petroleum Hydrcarbons (PHC) in Soil: Scientific Rationale

GC-FID/EI

TPH

Background to suite of analysis techniques for hydrocarbons in soil

27

Reference Method for CanadaWide Standard for Petroleum Hydrcarbons (PHC) in Soil- Tier 1 Method

GC-FID/EI

TPH

Reference method to above for hydrocarbons in soil

27

CCME

Introduction to Oil Chemical Analysis

Title

Chapter | 5

Standards Organization

103

104

PART | III

Oil Analysis and Remote Sensing

FIGURE 5.6 Illustration of the effect of oil weathering on the chromatograms. As the oil weathers, more and more of the lower n-alkanes are lost to evaporation, the most important component of weathering. Figure A shows the unweathered oils with the Cn Benzenes and naphthalenes marked. After about 30% loss of oil through evaporation, the Cn Benzenes are lost and also many of the lower alkanes as shown in Figure B. After 44.5% of the mass is evaporated as shown in Figure C, even the naphthalenes are not clearly present. It would be very difficult to compare weathered and unweathered oils simply by comparing the chromatograms.

fingerprinting in characterizing oils in terms of (1) the type(s) of precursor organic matter in the source rock (such as bacteria, algae, or higher plants); (2) correlation of oils with their source rocks; (3) determination of depositional

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environmental conditions (such as marine, terrestrial, deltaic, or hypersaline environments); (4) assessment of thermal maturity and thermal history of oil and the degree of oil biodegradation; and (5) providing information on the age of the source rock for petroleum. For example, oleanane (C30H52) is a biomarker characteristic of angiosperms (flowering plants) found only in Tertiary and Cretaceous (<130 million years) oils.35 The conversion of precursor biochemical compounds from living organisms into biomarkers creates a vast suite of compounds in crude oils that have distinct structures. Due to the wide variety of geological conditions under which oil has formed, every crude oil exhibits a unique biomarker fingerprint. Biomarkers can be detected in very low quantities (ppm and below) in the presence of many other types of petroleum hydrocarbon by using GC-MS. Relative to other hydrocarbon groups such as alkanes and many aromatic compounds, biomarkers are highly stable and degradation-resistant. Therefore, the usefulness of analyzing biomarkers is that they generate information useful in determining the source of spilled oil, monitoring the degradation process, and weathering the state of oils. They have proven useful in identifying petroleum-derived contaminants in the marine environment.22,36-38 In the past decades, the use of biomarker fingerprinting techniques to study spilled oils has rapidly increased, and biomarker parameters have been playing a prominent role in almost all oil-spill forensic investigations. Table 5.1 lists some typical biomarkers used in forensic analysis. The concentration of the biomarkers is important, while the ratio of various biomarkers in an oil is extremely useful in making the comparisons. The ratios of the biomarker contents in most oils are constant throughout processes such as weathering and biodegradation, and thus serve as an identity tag for an oil.

5.4.2. Sesquiterpanes and Diamondoids The commonly used biomarkers that occur within crudes and heavier refined products include pentacyclic triterpanes (e.g., hopanes), regular and rearranged steranes, and mono- and tri-aromatic steranes.2,39,40 However, the high boiling point pentacyclic triterpanes and steranes are generally absent or in very low abundances in lighter petroleum products such as jet fuels and midrange diesels. For lighter petroleum products, refining processes have removed most high-molecular-weight biomarkers from the crude oil feed stocks, while the smaller compounds of bicyclic sesquiterpanes are greatly concentrated in these petroleum products. Figure 5.7 shows some of the typical biomarkers used for oil identification. Sesquiterpanes are ubiquitous components of crude oils and ancient sediments. Bicyclic sesquiterpanes are also widely found in intermediate petroleum distillates and finished petroleum products. The abundance of the three major compounds in the m/z 123 chromatograms has been employed to differentiate the organic matter input from various sedimentary environments. Early studies

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hopane - general

Steranes

general sterane structure

Sesquiterpanes

8 (H) - drimane

Diamondoid

Adamantane

Oil Analysis and Remote Sensing

FIGURE 5.7 Illustration of some typical biomarkers used for oil identification.

focused mainly on geological application of sesquiterpane compounds. The naturally occurring bicyclic sesquiterpanes are stable in biodegradation, and therefore in recent years, they found potential applications in oil-source correlation and differentiation. Recently, environmental scientists have also considered fingerprinting the diamondoid hydrocarbons as a promising forensic technique for oil spill studies.41-47 These naturally occurring compounds are thermodynamically stable, and therefore, they may have potential applications both in oil-source correlation and differentiation for those cases where the traditional biomarker terpanes and steranes are absent due to removal in the refining processes. There is increased awareness of possible application of diamondoid compounds for source identification. Diamondoids have the general molecular formula C4nþ6H4nþ12 and are a class of saturated hydrocarbons that consist of threedimensional fused cyclohexane rings, which results in a diamond-like structure. The simplest compound of these polycyclic diamondoids is adamantane (C10H16), followed by its homologues diamantane (C14H20), tri-, tetra-, penta-, and hexamantane. Adamantine and diamantane and their various substituted equivalents are widely found in crude oils, intermediate petroleum distillates, and other petroleum products. Diamondoid compounds (adamantanes and diamantanes) in petroleum are believed to be the result of carbonium ion rearrangements of suitable cyclic precursors on clay substances in the source rock. The higher homologues of diamondoids are considered to be formed from

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the smaller diamondoid compounds under extreme temperature and pressure conditions.

5.5. FIELD ANALYSIS Analysis performed in the field is faster and more economical than analysis done in a laboratory.8 As analytical techniques are constantly improving and lighter and more portable equipment is being developed, more analytical work can be carried out directly in the field. Test kits have also been developed that can measure total petroleum hydrocarbons directly in the field. Some new methods use enzymes that are selectively affected by oil components. While these test kits are less accurate than laboratory methods, they are a rapid screening tool that minimize laboratory analysis and may provide adequate data for making response decisions. It is important to stress, however, that these field kits may have limitations and that results should be verified by laboratory analysis.

REFERENCES 1. Environment Canada, Analytical Method for the Determination of Individual and Total Petroleum Hydrocarbons, PAHs and Biomarkers in Oil and Oil-Spill-Related Environmental Samples (ETC Standard Method 5.3/1.4/M), Environment Canada, 2003. 2. Wang Z, Fingas M. Oil and Petroleum Product Fingerprinting Analysis by Gas Chromatographic Techniques, Chapter 27. In: Nollet Leo ML, editor. Chromatographic Analysis of the Environment, 1027. Boca Raton, FL: Taylor and Francis; 2005. 3. Wang ZD, Fingas M, Page D. Oil Spill Identification: Review. J Chromatogr 1999;369. 4. Wang ZD, Fingas M. Development of Oil Hydrocarbon Fingerprinting and Identification Techniques. Mar Pollut Bull 2003;423. 5. Wang ZD, Hollebone B, Fingas M, Fieldhouse B, Sigouin L, Landriault M, et al. Development of a Composition Database for Selected Multicomponent Oils. Vancouver: Environment Canada, 2002. 6. Wang Z, Fingas M, Sigouin L, Yang C, Hollebone B. Comparison Study for the CCME Reference Method for Determination of PHC in Soil by Using Internal and External Standard Methods and by Using Silica Gel Column Cleanup and in-situ Silica Gel Cleanup Methods. AMOP 2003;193. 7. Encyclopedia of Chemistry. New York: Wiley-Interscience; 2005. p. 241. 8. Lambert P, Fingas MF, Goldthorp M. An Evaluation of Field Total Petroleum Hydrocarbon (TPH) Systems. J Haz Mat 2001;65. 9. Fingas MF, Kyle DA, Lambert P, Wang Z, Mullin JV. Analytical Procedures for Measuring Oil Spill Dispersant Effectiveness in the Laboratory. AMOP 1995;339. 10. Index to EPA Test Methods, April 2003 revised edition, http://epa.gov/, accessed 2010. 11. Lambert P, Goldthorp M, Fieldhouse B, Wang Z, Fingas M, Pearson L, et al. A Review of Oil-in-Water Monitoring Techniques. IOSC 2001;1375. 12. Wang ZD, Fingas M, Li K. Fractionation of ASMB Oil, Identification and Quantitation of Aliphatic Aromatic and Biomarker Compounds by GC/FID and GC/MSD (Part I). J Chromatogr Sci 1994;361.

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13. Blomberg J, Schoenmakers PJ, Brinkman UA. Gas Chromatographic Methods for Oil Analysis. J Chromatogr 2002;137. 14. Beens J, Brinkman UA. The Role of Gas Chromatography in Compositional Analyses in the Petroleum Industry. TrACdTrend Anal 2000;260. 15. Wang Z, Fingas M, Sigouin L. Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Similar Chemical Composition. Environ Forens 2002;251. 16. Wang Z, Fingas M. Oil Spill Fingerprinting. J Int Soc Environ Forens 2002;3/4. 17. Wang Z, Fingas M, Yang C, Christensen JH. Crude Oil and Refined Product Fingerprinting: Principles, Chapter 16. In: Morrison Robert D, Murphy Brian L, editors. Environmental Forensics; Contaminant Specific Guide, 339. Amsterdam: Academic Press/ Elsevier; 2006. 18. Wang Z, Yang C, Hollebone BP, Fingas M, Lambert P, Landriault M. Characterization and Identification of Spill Samples from Recent Spill Incidents. AMOP 2006;113. 20. Daling PS, Faksness LG, Hansen AB, Stout SA. Improved and Standardized Methodology for Oil Spill Fingerprinting. Environ Forens 2002;263. 21. Stout SA, Uhler AD, Naymik TG, McCarthy KJ. Environmental Forensics: Unravelling Site Liability. Environ Sci Techn 1998;260A. 22. Stout SA, Uhler AD, McCarthy KJ, Emsbo-Mattingly S. Chapter 6: Chemical Fingerprinting of Hydrocarbons. In: Murphy BL, Morrison RD, editors. Introduction to Environmental Forensics, 137. London: Academic Press; 2002. 23. Wang ZD, Yang C, Fingas M, Hollebone B, Peng XZ, Hansen AB, et al. Characterization, Weathering, and Application to Source Identification of Spilled Oils. Environ Sci Technol 2005;8700. 24. ASTM standards, http://www.astm.org. 25. API test methods, http://www.api.org/Publications. 26. Index to EPA Test Methods, April 2003 revised edition, http://www.epa.gov. 27. CCME test methods, http://www.ccme.ca/publications. 28. Wang ZD, Fingas M. Study of the Effects of Weathering on the Chemical Composition of a Light Crude Oil Using GC/MS and GC/FID. J Microcolumn Sep 1995;617. 29. Wang ZD, Fingas M, Sergy G. Chemical Characterization of Crude Oil Residues from an Arctic Beach by GC/MS and GC/FID. Environ Sci Techn 1995;2622. 30. Wang ZD, Fingas M, Blenkinsopp S, Sergy G, Landriault M, Sigouin L, et al. Comparison of Oil Composition Changes Due to Biodegradation and Physical Weathering in Different Oils. J Chromatogr 1998;89. 31. Douglas GS, Bence AE, Prince RC, McMillen SJ, Butler EL. Environmental Stability of Selected Petroleum Hydrocarbon Source and Weathering Ratios. Environ Sci Technol 1996;2332. 32. Wang Z, Fingas M, Yang C, Hollebone B, Peng X. Biomarker Fingerprinting: Applications and Limitations for Source Identification and Correlation of Oils and Petroleum Products. AMOP 2004;103. 33. Wang Z, Yang C, Fingas M, Hollebone B, Yim U, Oh J. Petroleum Biomarker Fingerprinting for Oil Spill Characterization and Source Identification, Chapter 3. In: Wang Z, Stout S, editors. Oil Spill Environmental Forensics: Fingerprinting and Source Identification, 73. Amsterdam: Academic Press; 2007. 34. Prince RC, Elmendorf DL, Lute JR, Hsu CS, Haith CE, Senius JD, et al. 17_(H), 21_(H)Hopane as a Conserved Internal Marker for Estimating the Biodegradation of Crude Oil. Environ Sci Techn 1994;142. 35. Peters KE, Moldowan JW. The Biomarker Guide: Interpreting Molecular Fossils in Petroleum and Ancient Sediments. Old Tappen, NJ Englewood Cliffs, NJ: Prentice Hall; 1993.

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36. Kvenvolden KA, Hostettler FD, Rosenbauer RW, Lorenson TD, Castle WT, Sugarman S. Hydrocarbons in Recent Sediment of the Monterey Bay, National Marine Sanctuary. Marine Geology 2002;181:101e13. 37. Kaplan IR, Galperin Y, Lu S, Lee RP. Forensic Environmental Geochemistry Differentiation of Fuel-types, Their Sources, and Release Time. Org Geochem 1997;289. 38. Volkman JK, Holdsworth DG, Neill GP, Bavor Jr HJ. Identification of Natural, Anthropogenic and Petroleum Hydrocarbons in Aquatic Environments. Sci Total Environ 1992;203. 39. Wang Z, Yang C, Fingas M, Hollebone B, Peng X, Hansen AB, et al. Characterization, Weathering and Application of Sesquiterpanes to Source Identification of Spilled Lighter Petroleum Products. Environ Sci Techn 2005;8700. 40. Stout SA, Uhler AD, McCarthy KJ. Middle Distillate Fuel Fingerprinting Using Drimanebased Bicyclic Sesquiterpanes, Environ. Forensics 2005;6:241e51. 41. Yang C, Wang ZD, Hollebone B, Peng X, Fingas M, Landriault M. GC/MS Quantitation Analysis of Diamondoid Compounds in Crude Oils and Petroleum Products. Environ Forensics 2006;7:292. 42. Yang C, Wang ZD, Hollebone BP, Fingas M, Peng X, Landriault M. GC/MS Quantitation of Diamondoid Compounds in Crude Oils and Petroleum Products. AMOP 2006;91. 43. Wang Z, Yang C, Hollebone B, Fingas M. Forensic Fingerprinting of Diamondoids for Correlation and Differentiation of Spilled Oil and Petroleum Products. Environ Sci Techn 2006;5636. 44. Yang C, Wang ZD, Hollebone BP, Peng X, Fingas MF, Landriault MA. GC/MS Quantitation of Diamondoid Compounds in Crude oils and Petroleum Products. Forensics 2006;370. 45. Dahl JE, Liu SG, Carlson RMK. Isolation and Structure of Higher Diamondoids, Nanometersized Diamond Molecules. Science 2003;96. 46. Grice K, Alexander R, Kagi R. Diamondoid Hydrocarbons as Indicators of Biodegradation in Australian Crude Oils. Org Geochem 2000;67. 47. Stout SA, Douglas GS. Diamondoid Hydrocarbons-Application in the Chemical Fingerprinting of Natural Gas Condensate and Gasoline. Environ Foren 2004;225.

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Chapter 6

Oil Spill Remote Sensing: A Review Merv Fingas and Carl E. Brown

Chapter Outline 6.1. Introduction 6.2. Visible Indications of Oil 6.3. Optical Sensors 6.4. Laser Fluorosensors 6.5. Microwave Sensors 6.6. Slick Thickness Determination 6.7. Acoustic Systems 6.8. Integrated Airborne Sensor Systems

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6.9. Satellite Remote Sensing 6.10. Oil under Ice Detection 6.11. Underwater Detection and Tracking 6.12. Small Remote-controlled Aircraft 6.13. Real-time Displays and Printers 6.14. Routine Surveillance 6.15. Future Trends 6.16. Recommendations

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6.1. INTRODUCTION Large spills of oil and related petroleum products in the marine environment can have serious biological and economic impacts. Public and media scrutiny is usually intense following a spill, with demands that the location and extent of the oil spill be determined. Remote sensing is playing an increasingly important role in oil spill response efforts. Through the use of modern remote-sensing instrumentation, oil can be monitored on the open ocean around the clock. With knowledge of slick locations and movement, response personnel can more effectively plan countermeasures in an effort to lessen the effects of the pollution. In recent years, there has been a strong interest in detection of illegal discharges, especially in view of the large seabird mortality associated with such discharges.1 Even though sensor design and electronics are becoming increasingly sophisticated and much less expensive, the operational use of remote-sensing Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10006-1 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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equipment lags behind the technology. In remote sensing, a sensor, other than the eye or conventional photography, is used to detect the target of interest at a distance. The most common forms of oil spill surveillance and mapping are still sometimes carried out with simple still or video photography. Remote sensing from an aircraft is still the most common form of oil spill tracking. Attempts to use satellite remote sensing for oil spills continue, although success is not necessarily as claimed and is generally limited to identifying features at sites where known oil spills have occurred or for mapping discharges or known spills. It is important to divide the uses of remote sensing into the end use or objective, as the utility of the sensor or sensor system is best defined that way. Remote-sensing systems for oil spills used for routine surveillance certainly differ from those used to detect oil on shorelines or land. A single tool does not serve for all functions. For a given nation and several functions, many types of systems may, in fact, be needed. Furthermore, it is necessary to consider the end use of the data. The end use of the data, be it location of the spill, enforcement, or support to cleanup, may also dictate the resolution or character of the data needed. Several general reviews of oil spill remote sensing have been prepared.2-7 These reviews show that although progress has been made in oil spill remote sensing, this progress has been slow. Furthermore, these reviews show that specialized sensors offer advantages to oil spill remote sensing. Off-the-shelf sensors have very limited application to oil spills.

6.2. VISIBLE INDICATIONS OF OIL Under many circumstances oil on the surface is not visible to the eye.8 Other than the obvious situations of nighttime and fog, in many situations oil cannot be seen. A very common situation is that of thin oil, such as from ship discharges, or

FIGURE 6.1 An example of problems in detecting slicks visually. There is no oil in this image. The differences in water color are caused by mineral fines at the top of the pictures and the meeting of darker water from the open ocean.

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FIGURE 6.2 Another example of confusion in the visible region. This anomaly is caused by the front between a river and seawater. Again there is no oil in this image.

FIGURE 6.3 An image of Herring “milk” on the water surface. This is often mistaken for oil in various sensors, and again there is no oil in this image.

the presence of materials, such as sea weed, ice, and debris, that mask oil presence. Often there are conditions on the sea that may appear like oil, when indeed there is no oil. These include wind shadows from land forms, surface wind patterns on the sea, surface dampening by submerged objects or weed beds, natural oils or biogenic material, and oceanic fronts. In the case of large spills, the area may be too great to be mapped visually. Several of these cases are illustrated in Figures 6.1 to 6.12. All of these factors dictate that remotesensing systems be used to assist in the task of mapping and identifying oil. In many cases, aerial observation and remote sensing are necessary to direct cleanup crews to slicks. Figure 6.13 shows a case where no aerial direction

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FIGURE 6.4 This image again shows no oil and shows open seawater at a front with mineralladen bay water.

FIGURE 6.5 An image of the Exxon Valdez tanker at Naked Island. The apparent oil is actually reflections from clean water and some wind ruffles on the sea. There is no oil in this image.

was given and a skimmer crew is missing the slick by about half a kilometer. Figure 6.14 shows a skimmer crew that was directed to the thicker slick in the area.

6.3. OPTICAL SENSORS 6.3.1. Visible The use of human vision alone is not considered remote sensing; however, it still represents the most common technique for oil spill surveillance. In the

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FIGURE 6.6 An image looking into a bay. The foreground material is oil; however what appears somewhat like oil further into the bay are actually surface wind calms.

FIGURE 6.7 An image of sheen from a major spill. One can see sheen to a distance of about 30 km and about 10 km wide. Large areas like this are hard to map without the aid of remote sensing.

past, major campaigns using only human vision were mounted with varying degrees of success.9 Optical techniques, using the same range of the visible spectrum detection, are the most common means of remote sensing. Cameras, both still and video, are common because of their low price and commercial availability. In recent years, visual or camera observation has been enhanced by the use of GPS (Global Positioning Systems).10 Systems are now available to directly map remote-sensing data onto base maps. In the visible region of the electromagnetic spectrum (approximately 400 to 700 nm), oil has a higher surface reflectance than water, but shows limited nonspecific absorption tendencies. Oil generally manifests throughout the

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FIGURE 6.8 An image of water from an airplane during foggy conditions. There is no oil in this image.

FIGURE 6.9 A visible image of a slick that had just been illegally discharged from a ship. The multiple colors are due to the light path interference and indicates a thickness of about 1 mm.

entire visible spectrum. Sheen shows up silvery and reflects light over a wide spectral region down to the blue. As there is no strong information in the 500 to 600 nm region, this region is often filtered out to improve contrast.11 Overall, however, oil has no specific characteristics that distinguish it from the background.12 Taylor studied oil spectra in the laboratory and the field and observed flat spectra with no usable features distinguishing it from the background.13 Therefore, techniques that separate specific spectral regions do not increase detection capability. Some researchers noted that while the oil spectra is flat, the presence of oil may slightly alter water spectra.14 It has been suggested that

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FIGURE 6.10 A visible image of a cleanup operation. Notice the various false indications of oil further away from the scene. Photography by Environment Canada.

FIGURE 6.11 An infrared image of a slick as taken in 1981. Note the annotation providing essential times and positions.

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FIGURE 6.12 A visible image of the same slick and at the same time as the one shown in Figure 6.11. This illustrates the higher capability that infrared imaging has under these specific conditions.

FIGURE 6.13 A visible image of a cleanup crew missing a slick by at least a half kilometer. The actual slick is noted on the image. Aerial direction of cleanup crews is not only desirable but necessary in many cases. Photography by Environment Canada.

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FIGURE 6.14 A visible image of a cleanup crew aiming toward the thickest slicks in the area as directed by an aerial surveillance team.

the water peaks are raised slightly at 570 to 590, 780 to 710, and 810 to 710 nm. At the same time there are depressions or troughs at 650 to 680 nm and 740 to 760 nm. It has been found that high contrast in visible imagery can be achieved by setting the camera at the Brewster angle (53 degrees from vertical) and using a horizontally aligned polarizing filter that passes only that light reflected from the water surface.15 This is the component that contains the information on surface oil.11 It has been reported that this technique increases contrast by up to 100%. Filters with band-pass below 450 nm can be used to improve contrast. View angle is important, and some researchers have noted that the thickness changes the optimal view angle.16 On land, hyperspectral data (use of multiple bands, typically 10 to 100) has been used to delineate the extent of an oil well blowout.17 The technique used was spectral reflectance in the various channels, as well as the usual black coloration. Video cameras are often used in conjunction with filters to improve the contrast in a manner similar to that noted for still cameras. This technique has had limited success for oil spill remote sensing because of poor contrast and lack of positive discrimination. Despite this, video systems have been proposed as remote-sensing systems.18 With new light-enhancement technology (low lux), video cameras can be operated even in darkness. Tests of a generation III night vision camera shows that this technology is capable of providing imagery in very dark night conditions.19,20 Scanners were used in the past as sensors in the visible region of the spectrum. A rotating mirror or prism sweeps the field-of-view (FOV) and directs the light toward a detector. Before the advent of CCD (charge-coupled device) detectors, this sensor provided much more sensitivity and selectivity than a video camera. Another advantage of scanners was that signals were

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digitized and processed before display. Recently, newer technology has evolved, and similar digitization can now be achieved without scanning by using a CCD imager and continually recording all elements, each of which is directed to a different FOVon the ground. This type of sensor, known as a pushbroom scanner, has many advantages over the older scanning types. It can overcome several types of aberrations and errors, the units are more reliable than mechanical ones, and all data are collected simultaneously for a given line perpendicular to the direction of the aircraft’s flight. Several types of scanners were developed. In Canada, the MEIS (Multidetector Electro-optical Imaging Scanner) and the CASI (Compact Airborne Spectrographic Imager) have been developed, and in the Netherlands, the Caesar system was developed.11, 21, 22 Digital photography has enabled the combination of photographs and the processing of images. Locke et al. used digital photography from vertical images to form a mosaic for an area impacted by an oil spill.23 It was then possible to form a singular image and to classify oil types by color within the image. The area impacted by the spill was also carried out. Video cameras are often used in conjunction with filters to improve the contrast in a manner similar to that noted for still cameras. This technique has had limited success for oil spill remote sensing because of poor contrast and lack of positive discrimination. The detection or measurement of oil in water has never been successfully accomplished using remote visible technology. There may be potential for light scattering technology. Stelmaszewski and coworkers measured the light scattering of crude oil in water emulsions and noted that scattering increases with wavelength in the UV range and decreases slightly with the wavelength of visible light.24 The use of visible techniques in oil spill remote sensing is largely restricted to documentation of the spill because there is no mechanism for positive oil detection. Furthermore, there are many interferences or false alarms. Sun glint and wind sheens can be mistaken for oil sheens. Biogenic material such as surface seaweeds or sunken kelp beds can be mistaken for oil. Oil on shorelines is difficult to identify positively because seaweeds look similar to oil and oil cannot be detected on darker shorelines. In summary, the usefulness of the visible spectrum for oil detection is limited. It is, however, an economical way to document spills and provide baseline data on shorelines or relative positions.

6.3.2. Infrared Oil, which is optically thick, absorbs solar radiation and reemits a portion of this radiation as thermal energy, primarily in the 8 to 14 mm region. In infrared (IR) images, thick oil appears hot, intermediate thicknesses of oil appear cool, and thin oil or sheens are not detected. The thicknesses at which these transitions occur are poorly understood, but evidence indicates that the transition between the hot and cold layer lies between 50 and 150 mm and the minimum

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detectable layer is between 10 and 70 mm.25-28 The reason for the appearance of the "cool" slick is not fully understood. A plausible theory is that a moderately thin layer of oil on the water surface causes destructive interference of the thermal radiation waves emitted by the water, thereby reducing the amount of thermal radiation emitted.8 This may be analogous to the appearance of the rainbow sheen, which is explained in Section 6.2. The cool slick would correspond to the thicknesses as observed above because the minimum destructive thickness would be about two times the wavelength, which is between 8 and 10 mm. This would yield a destructive onset of about 16 to 20 mm to about 4 wavelengths or about 32 to 40 mm. The destructive area is usually only seen with test slicks, which is explained by the fact that the more rapidly spreading oil is more transparent than the remaining oil. The onset of the hot thermal layer would in theory then be at thicknesses greater than this or at about 50 mm. IR devices cannot detect emulsions (water-in-oil emulsions) under most circumstances.29 This is probably a result of the high thermal conductivity of emulsions as they typically contain 70% water and thus do not show a temperature difference. IR cameras are now very common, and commercial units are available from several manufacturers. In the past, scanners with IR detectors were largely used. A disadvantage of the older type of IR detector, however, is that they required cooling to avoid thermal noise, which would overwhelm any useful signal. Liquid nitrogen, which provides about 4 hours of service, had traditionally been used to cool the detector. Some, smaller sensors use closed-cycle or Sterling coolers, which operate on the cooling effect created by expanding gas. While a gas cylinder or compressor must be transported with this type of cooler, refills or servicing may not be required for days at a time.30 In recent times, uncooled detectors are commonplace and have entirely replaced the older, cooled detectors. Most IR sensing of oil spills takes place in the thermal IR at wavelengths of 8 to 14 mm. A slightly different sensor, which is designed as a fixed-mounted unit, uses the differential reflectance of oil and water at 2.5 and 3.1 mm.31 Tests of a mid-band IR system (3.4 to 5.4 mm) over the Tenyo Maru oil spill showed no detection in this range, but ship scars were visible.32-34 Specific studies in the thermal IR (8 to 14 mm) show that there is no spectral structure in this region.35 Tests of a number of IR systems show that spatial resolution is extremely important when the oil is distributed in windrows and patches, emulsions are not always visible in the IR, and cameras operating in the 3 to 5 mm range are only marginally useful.36 Nighttime tests of IR sensors show that there is detection of oil (oil appears cold on a warmer ocean), however, the contrast is not as good as during daytime.36-38 The relative thickness information in the thermal IR can be used to direct skimmers and other countermeasure equipment to thicker portions of the slick. Figures 6.11, 6.12, 6.15, and 6.16 illustrate the utility of IR oil imaging. Oil

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FIGURE 6.15 An image of an oil slick formed from a composite of infrared and ultraviolet images. The red represents the thermal infrared and the thickest oil. The darker spots are intermediate thicknesses. The light blue area represents thin oil or sheen and is taken from the ultraviolet image.

FIGURE 6.16 A composite image of the infrared and ultraviolet images of a slick similar to that in Figure 6.11. The outlined areas are from the infrared sensor and represent the thicker oil. Areas of the infrared and ultraviolet sensors are also annotated.

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detection in the IR is not positive, however, as several false targets can interfere, including seaweed, shoreline, and oceanic fronts.39 IR is reasonably inexpensive, however, and is currently the prime tool used by the spill remote-sensor operator.

6.3.3. Ultraviolet Ultraviolet (UV) sensors can be used to map sheens of oil as oil slicks display high reflectivity of UV radiation even at thin layers (<0.1 mm). Overlaid UV and IR images are often used to produce a relative thickness map of oil spills. This has been illustrated in Figures 6.15 and 6.16. UV cameras, though inexpensive, are not often used in this process, however, as it is difficult to overlay camera images.30 Data from IR scanners and derived from push-broom scanners can be easily superimposed to produce these IR/ UV overlay maps. UV data are also subject to many interferences or false images such as wind slicks, sun glints, and biogenic material. Since these interferences are often different from those for IR sensing, combining IR and UV can provide a more positive indication of oil than using either technique alone.

6.4. LASER FLUOROSENSORS Laser fluorosensors are sensors that take advantage of the fact that certain compounds in petroleum oils absorb UV light and become electronically excited. This excitation is rapidly removed through the process of fluorescence emission, primarily in the visible region of the spectrum. Since very few other compounds show this tendency, fluorescence is a strong indication of the presence of oil. Natural fluorescing substances, such as chlorophyll, fluoresce at sufficiently different wavelengths than oil to avoid confusion. As different types of oil yield slightly different fluorescent intensities and spectral signatures, it is possible to differentiate between classes of oil under ideal conditions.40-50 Readers are referred to a separate subsection in this book for a review of laser fluorosensors. This section on remote sensing will just give a brief introduction. Most laser fluorosensors used for oil spill detection employ a laser operating in the UV region of 300 to 355 nm.40,50-52 With this wavelength of activation, there exists a broad range of fluorescent response for organic matter, centered at 420 nm. This is referred to as Gelbstoff or yellow matter, which can be easily annulled. Chlorophyll yields a sharp peak at 685 nm. The fluorescent response of crude oil ranges from 400 to 650 nm with peak centers in the 480 nm region. The use of laser fluorosensors for chlorophyll and other applications has been well documented.53 One laser fluorosensor operating at 488 nm from an Argon ion laser was successful in detecting oil from a ship platform.54

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Another phenomenon, known as Raman scattering, involves energy transfer between the incident light and the water molecules. When the incident UV light interacts with the water molecules, Raman scattering occurs. This involves an energy transfer between the incident light and the water molecules. The water molecules absorb some of the energy as rotational-vibrational energy and emit light at wavelengths, which are the sum or difference between the incident radiation and the vibration-rotational energy of the molecule. The Raman signal for water occurs at 344 nm when the incident wavelength is 308 nm (XeCl laser). The water Raman signal is useful for maintaining wavelength calibration of the fluorosensor in operation, but it has also been used in a limited way to estimate oil thickness because the strong absorption by oil on the surface will suppress the water Raman signal in proportion to thickness.55,56 The point at which the Raman signal is entirely suppressed depends on the type of oil, since each oil has a different absorption coefficient. The Raman signal suppression has led to estimates of sensor detection limits of about 0.05 to 0.1 mm.57 The principle of fluorescence can also be used on a smaller scale. A handheld UV light has been developed to detect oil spills at night at short range.58 Another related instrument is the Fraunhofer Line Discriminator, which is essentially a passive fluorosensor using solar irradiance instead of laser light.11 This instrument was not very successful because of the limited discrimination and the low signal-to-noise ratio. Laser fluorosensors have significant potential as they may be the only means to discriminate between oiled and unoiled seaweed and to detect oil on different types of beaches. Tests on shorelines show that this technique has been very successful.59 Algorithms for the detection of oil on shorelines have been developed.60 Work has been conducted on detecting oil in the water column, such as occurs with the product, Orimulsion.61-65 The fluorosensor is also the only reliable means of detecting oil in certain ice and snow situations. Operational use shows that the laser fluorosensor is a powerful tool for oil spill remote sensing.19,43

6.5. MICROWAVE SENSORS 6.5.1. Radiometers Microwave radiometers detect the presence of an oil film on water by measuring an interference pattern excited by the radiation from free space. The apparent emissivity factor of water is 0.4 compared to 0.8 for oil.11,66 This passive device can detect this difference in emissivity and could therefore be used to detect oil. In addition, as the signal changes with thickness, in theory, the device could be used to measure thickness. This detection method has not been very successful in the field, however, as several environmental and oilspecific parameters must be known. In addition, the signal return is dependent

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on oil thickness but in a cyclical fashion. A given signal strength can imply any one of two or three signal film thicknesses within a given slick. Microwave energy emission is greatest when the effective thickness of the oil equals an odd multiple of one quarter of the wavelength of the observed energy. Biogenic materials also interfere, and the signal-to-noise ratio is low. In addition, it is difficult to achieve high spatial resolution (might need resolution in meters rather than the typical tens of meters for a radiometer).67 The Swedish Space Agency has carried out work with different systems, including a dual-band, 22.4- and 31-GHz device, and a single band 37-GHz device.68 Skou, Sorensen, and Poulson describe a two-channel device operating at 37.5 and 10.7 GHz.69 Mussetto and coworkers at TRW described the tests of 44-94-GHz and 94-154-GHz, two-channel devices over oil slicks.70 They showed that correlation with slick thickness is poor and suggest that factors other than thickness also change surface brightness. They suggest that a singlechannel device might be useful as an all-weather, relative-thickness instrument. Tests of single-channel devices over oil slicks have also been described in the literature, specifically a 36-GHz and a 90-GHz device.71,72 A new method of microwave radiometry has recently been developed in which the polarization contrasts at two orthogonal polarizations are measured in an attempt to measure oil slick thickness.73 A series of frequency-scanning radiometers have been built and appear to have overcome the difficulties with the cyclical behavior.74,75 In summary, passive microwave radiometers may have potential as allweather oil sensors. Their potential as a reliable device for measuring slick thickness, however, is uncertain at this time.

6.5.2. Radar Capillary waves on the ocean reflect radar energy, producing a “bright” image known as sea clutter. Since oil on the sea surface dampens capillary waves, the presence of an oil slick can be detected as a “dark” sea or one with an absence of this sea clutter.76 Unfortunately, the oil slick is not the only phenomenon detected in this way. There are many interferences or false targets, including freshwater slicks, wind slicks (calms), wave shadows behind land or structures, seaweed beds that calm the water just above them, glacial flour, biogenic oils, and whale and fish sperm.77-81 As a result, radar can be ineffective in locations such as Prince William Sound, Alaska where dozens of islands, freshwater inflows, ice, and other features produce hundreds of such false targets. Despite these limitations, radar is an important tool for oil spill remote sensing because it is the only sensor that can be used for searches of large areas and it is one of the few sensors that can “see” at night and through clouds or fog. Figures 6.17 to 6.23 illustrate the many slick look-alikes that appear in radar displays.

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FIGURE 6.17 Airborne radar image of a small test slick attended by two boats. Note that the boats cast a radar shadow on both their sides. A ship is passing to the top right of the image, and the ship’s wake also casts a radar shadow.

FIGURE 6.18 A satellite Radarsat-I image of a large area of sea during the raising of the Irving Whale barge. Note that the area to the left that appears darker is caused by wind shadows and low winds. Only the small areas noted are actually slicks. One might have to know beforehand where the slicks were before interpreting this image.

The two basic types of imaging radar that can be used to detect oil spills and for environmental remote sensing in general are Synthetic Aperture Radar (SAR) and Side-Looking Airborne Radar (SLAR). SLAR is an older but less expensive technology that uses a long antenna to achieve spatial resolution. SAR uses the forward motion of the aircraft to synthesize a very long antenna, thereby achieving very good spatial resolution, which is independent of range, with the disadvantage of requiring sophisticated electronic processing. Though inherently more expensive, the SAR has greater range and resolution than the

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FIGURE 6.19 A close-up of the area shown in Figure 6.18 from radar satellite. These dark areas are actually oil, as confirmed by ground observation. The white spots in the center are ships.

FIGURE 6.20 An image of the source of the oil shown in Figure 6.19. The ships shown here appear as white spots in the radar image in Figure 6.19. Photography by Environment Canada.

SLAR. In fact, comparative tests show that SAR is vastly superior.82-84 Search radar systems, such as those frequently used by the military, cannot be used for oil spills because they usually remove the clutter signal, which is the primary signal of interest for oil spill detection. Furthermore, the signal processing of this type of radar is optimized to pinpoint small, hard objects, such as periscopes. This signal processing is very detrimental to oil spill detection.

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FIGURE 6.21 A radar satellite image of a coastline. There is no oil in this image. The track through the image is the wake of a vessel. It should be noted that all ship wakes leave a shadow like this, making it very hard to use radar to detect ship discharges. The dark areas near the coastline are low-wind areas, probably caused by the coast wind shadows.

FIGURE 6.22 A view of an area near ships and platforms. A possible slick is pointed out; however, as it is very near a major low-wind area, it is difficult to say whether or not this is really a slick.

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FIGURE 6.23 A view of the track of a vessel. Despite the interpretation that there was a slick behind the vessel, the black line may be simply a ship wake. Note also the other dark areas from low winds and coast wind shadows.

SLAR has predominated airborne oil spill remote sensing, primarily because of the lower price.85,86 There is some recognition among the operators that SLAR is very subject to false hits, but solutions are not offered. Experimental work on oil spills has shown that X-band radar yields better data than L- or C-band radar.87,88 It has also been shown that vertical antenna

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polarizations for both transmission and reception (VV) yield better results than other configurations.82,89-91 The ability of radar to detect oil is also limited by sea state. Sea states that are too low will not produce enough sea clutter in the surrounding sea to contrast to the oil, and very high seas will scatter radar sufficiently to block detection inside the troughs. Indications are that minimum wind speeds of 1.5 m/s (~3 knots) are required to allow detectability, and a maximum wind speed of 6 m/s (~12 knots) will again remove the effect.92-94 The most accepted limits are 1.5 m/s (~3 knots) to 10 m/s (~20 knots). This limits the environmental window of application of radar for detecting oil slicks. Gade et al. studied the difference between extensive systems from a space-borne mission and a helicopter-borne system.95 They found that at high winds, it was not possible to discriminate biogenic slicks from oil. At low-wind speeds, it was found that images in the L-band showed discrimination. Under these conditions, the biogenic material showed greater damping behavior in the L-band. Okamoto et al. studied the use of ERS-1 using an artificial oil (oleyl alcohol) and found that an image was detected at a wind speed of 11m/s, but not at 13.7 m/s.96 SAR can be polarimetric imaging that is horizontal-horizontal (HH), vertical-vertical (VV), and cross combinations of these. Several researchers have shown that VV is best for oil spill detection and discrimination.97-100 Migliaccio et al. showed that the co-polarized phase differencedfor example, the difference between the HH and VV phases can be used to discriminate oil FIGURE 6.24 An HH polarized view of the sea surface.

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131 FIGURE 6.25 A VV polarized view of the same area of sea surface. Note that this polarization yields a slightly clearer image of sea-surface details than shown in Figure 6.24.

slicks from biogenic slicks.97 A larger standard deviation for the slick, compared to the sea, typically indicates that it is oil. Figures 6.24 and 6.25 show the difference between a VV and HH polarization. Radar has also been used to measure currents and predict oil spill movements by observing frontal movements.101 Work has shown that frontal currents and other features can be detected by SAR.102 Shipborne radar has similar limitations and the additional handicap of low altitude, which restricts its range to between 8 and 30 km, depending on the height of the antenna. Ship radars can be adjusted to reduce the effect of sea clutter deenhancement. Shipborne radar successfully detected a surface slick in the Baltic Sea from 8 km away and during a trial off the coast of Canada at a maximum range of 17 km.103 During the Prestige spill, a Netherlands vessel successfully used this technique to guide a recovery vessel into slicks. The technique is, however, very limited by sea state, and in all cases where it was used, the presence and location of the slick were already known or suspected. Recently, researchers have carried out work on improving the imaging of slicks from shipborne radars.104 Today there are some commercial products that enhance the images from shipborne radar to enable some oil imaging. Gangeskar has proposed an automatic system that can be mounted on oil drilling platforms.105 This system would use standard X-band ship navigation units and would provide an alert if an oil spill was present. The system includes

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an extensive postprocessing system to provide both a user-friendly GUI and an automatic detection and alert system. The system has not been fully tested to date. In summary, radar optimized for oil spills is useful in oil spill remote sensing, particularly for searches of large areas and for nighttime or foul weather work. The technique is highly prone to false targets, however, and is limited to a narrow range of wind speeds. Because of the all-weather and daynight capability, radar is now the most common means of remote sensing.

6.5.2.1. Radar Processing Because radar detection of oil spills is so highly susceptible to false images, much work has taken place on means to differentiate oil slicks and false targets, often called look-alikes. These look-alikes include: low-wind areas, areas sheltered by land, rain cells, organic films, grease ice, wind fronts, up-welling zones, oceanic fronts, algae blooms, current shear zones, and so on.106 The discussion in this subsection is relevant to both satellite and airborne SAR systems. Several “automatic” systems have been designed for slick detection.107 Limited testing with actual satellite output has shown that many false signals are present in most locations.108,109 Extensive effort on data processing appears to improve the chances of oil detection.110 In recent years, automatic systems have given way to systems involving smart algorithms that are manipulated by operators.111-113 The most common way to eliminate wind-origin look-alikes is to map the wind fields in the same coordinates as the radar data.110,114 The most common slick look-alikes are low-wind areas. One group of researchers used radar wind data calibrated to wind data from an ocean buoy to map oil seeps in the southern Gulf of Mexico.114 Most researchers used some form of neural networks or fuzzy logic to assist in the discrimination of look-alikes and the intended targets.115-118 Others used various forms of models such as range dependence models.119 Topouzelis and coworkers developed several series of mathematical networks for differentiating slicks from look-alikes.120-123 The basis of these networks is the idea that generally oil slicks are imaged through a complex series of processes and conditions. Thus imaging is not a simple statistical manipulation. The same group developed a fuzzy classification to differentiate look-alikes from oil spills. The methodology involved four procedures. The first is the segmentation of the image into large image segments with different statistical values. In the second procedure, a detailed scale segmentation is carried out, and statistical values of each segment are compared to the threshold of the large segment from which it came. Third, the dark portions are classified according to the properties of the surrounding areas. Finally, the dark areas are classified using knowlege bases. The group also examined the use of

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forward-feed neural networks to discriminate slicks from look-alikes.120,124 Several topologies of forward-feed neural networks were examined, and none were better than others. The networks yielded classification accuracies as high as 91.3 to 93.6% for the given example. A recent work by Topouzelis used the inputs of shape texture, asymmetry, mean difference to neighbours, and power to the mean images in a neural network.106 The workers used forward-feed neural networks. It was found that the classification accuracy was 99.4% for the MLP network in the test case. Later, Topouzelis and co workers used a similar method to test a data set of 69 oil spills and 90 look-alikes.122 They found a combination of 11 features out of a possible 25 features. The 11 features found to be best for discrimination are perimeter, shape factor object mean value, ratio of the power to mean ratios, local area contrast ratio, mean border gradient, maximum border gradient, standard deviation border gradient, maximum border gradient, mean difference to neighbors, and spectral texture. Use of these factors resulted in classification accuracies of 85.3% for oil spills and 84.4% for look-alikes. A similar approach is to use a classification scheme that incorporates some of the same input parameters. Karantzalos and Argialas proposed a classification scheme involving processes and then a classification scheme. The first processing step involves filtering and levels.125 The second step is segmentation of the images to include all suspected slicks. The final step is to classify the potential slicks according to area, perimeter, shape complexity eccentricity, orientation, segment mean border gradient, inside segment standard deviation, and outside segment standard deviation. Several researchers have used Geographic Information System (GIS) databases to assist in the interpretation of SAR imagery.125-131 The technique divides the area of interest into segments and notes data such as currents, proximity to land, wind, and sea lanes. These parameters are then correlated to the SAR images. For example, oil spills are much more likely under the correct wind conditions, in sea lanes, and far from land. Tahvonen used data sets including wind speed and direction, sea-surface temperature, heavy rain, and location of algae blooms to assist in the discrimination.126 Muellenhoff proposed a data set consisting of wind information, sea-surface temperature, chlorophyll-a concentration, geostrophic currents, wave information, contextual background information, and existing oil spill databases.129 The assigned influences were wind speedd30%; wind directiond12%; sea-surface temperatured14%, chlorophyll-a concentrationd10%; oil portsd10%; and main traffic linesd20%. Wave and current direction only accounted for 2% each. Migliaccio and group studied the processing of SAR images from an aircraft-based sensor.132-134 It was noted that the main obstacle to analysis was speckle in the images. Speckle is caused by stray reflectances, such as from rough seas. Speckle is also caused by random constructive and destructive interference. Since speckle is temporary, multi-look imaging is one way to decrease speckle by a large amount. Further processing can then be achieved by

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combining multi-look data with wind data, best obtained from satellite scatterometers. The technique proposed for multi-look data is to divide the SAR imagery into subbands and then generate lower-resolution imagery. Then the images are averaged. This results in reduction of speckle. To process singlelook data with high speckle content, filters are used. First speckle is removed, and then an ROA (ratio of average) filter is used. In both techniques, edge detection is used to find the actual limits of the slicks or look-alikes. Marghany and co workers used a fractal method to analyze SAR data.135 The images are broken into fractals, and these fractals have dimensions that are different for oil spills and look-alikes. A further study under different wind speeds showed that there were differences only in the wide beam mode for lowwind zones and current shear features between real oil slicks.136 Danisi et al. utilized a similar approach.137 Another method employed by researchers to separate oil slicks from lookalikes is to use textural analysis.138,139 Direct statistical methods are also employed. Tello et al. noted that an algorithm characterizing the border between oil spill candidates and the surrounding sea allows for good classification.139 Lounis et al. used a measure of similarity between the local probability density function of clean water and of the dark area to be examined.140 Comparing the two values is said to result in discrimination between oil and look-alikes. Pelizzari employed a similar technique using graph cuts to estimate a smoothness factor.141 Ferraro et al. describe the development of an operational system for the Mediterranean Sea and show a procedure for identifying oil spills as (1) isolation and contouring of all dark signatures, (2) extraction of shape and backscattering contrast signatures, (3) test of these values against standard values, and (4) calculation of the probabilities of each patch.142-144 Another series of techniques involves the use of two streams of information. Several researchers used both SAR and visible information from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellites to discriminate between look-alikes and oil slicks.145 The visible imagery is subject to false images, but not the same ones as satellites, and thus discrimination can be achieved to a degree. Similarly, Sipelgas used visible imagery from the MODIS satellite to assist in discrimination of false images from oil slicks in the Gulf of Finland.146 Adamo et al. used three streams for informationdSAR data, MODIS, and MERIS datadto discriminate look-alikes from actual spills.145

6.5.3. Microwave Scatterometers A microwave scatterometer is a device that measures the scattering of radar energy by a target. One radar scatterometer was flown over several oil slicks and used a low-power transmitter operating in the Ku band (13.3 GHz).11 The scatterometer detected the oil, but discrimination was poor. The “Heliscat,” a device with five frequencies, has been used to investigate capillary wave

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damping.92 The advantage of a microwave scatterometer is that it has an aerial coverage similar to optical sensors and it can look at several incident angles. The main disadvantages include the lack of discrimination for oil and the lack of imaging capability.

6.5.4. Surface Wave Radars It is possible to send radio waves along the sea using high frequency. The conductivity of the sea acts as a form of wave guide. These radars can be used to detect ships as far out as 500 km.147 Since these are surface wave phenomena, only targets above the surface are detected; thus slicks may not be detected by this technique.148 Modeling of the technique does not show whether there is potential for this method.149

6.5.5. Interferometric Radar Radars can be used to measure height, currents, and other surface elevation phenomena using interferometric techniques. Some radar systems on aircraft are fitted for this application, such as the government of Canada Convair 580. This can also be carried out in space using two satellites traveling in tandem. One research group employed the tandem satellite pairs of ERS-2 and ENVISAT to carry out such work, but there are no reports on the use on oil spills.150

6.6. SLICK THICKNESS DETERMINATION There has long been a need to measure oil slick thickness; this need has been expressed both within the oil spill response community and among academics in the field. There are presently no reliable methods, either in the laboratory or in the field, for accurately measuring oil-on-water slick thickness. The ability to do so would significantly increase understanding of the dynamics of oil spreading and behavior. Knowledge of slick thickness would make it possible to determine the effectiveness of certain oil spill countermeasures, including dispersant application and in-situ burning. Indeed, the effectiveness of individual dispersants could be determined quantitatively if the oil remaining on the water surface following dispersant application could be accurately measured.151,152

6.6.1. Visual Thickness Indications A very important tool for working with oil spills has been the relationship between appearance and thickness. Careful study of the literature on this relationship and comparison of this to field experience shows that there is limited potential to scale thicknesses to visual appearance.8 The only physical-based appearances that occurs are thicknesses of about 0.7 to 2.5 mm

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TABLE 6.1 Relationship of Thickness to Appearance Visibility Thresholds (mm)

Typical thickness

Darkening Minimum Silvery Rainbow Colors

Dull Colors

Dark

0.09

2.7

8.5

0.1

0.6*

0.9

*Note this is the only physical-based appearance factor

FIGURE 6.26 A rainbow sheen above a sunken vessel. The appearance of a rainbow sheen is the only strong visible indicator of slick thickness, and thickness may be between about 0.7 and 2.5 mm. Photography by Environment Canada.

at which the rainbow colors appear as a result of multiple constructive and destructive interferences by light. Table 6.1 presents a summation of the best knowledge on this phenomenon. Figures 6.26 and 6.27 show typical rainbow sheens for which we can estimate that the thickness is about 1 mm. This is the only color appearance that has a physical slick thickness associated with it.

6.6.2. Slick Thickness Relationships in Remote Sensors A number of investigators tried to correlate slick thickness with appearance in various remote-sensing instruments. Hollinger and Mennella conducted a series of eight controlled oil spills off Virginia to investigate the use of microwave radiometry to delineate oil spills.153 They used 19.4 and 69.8 GHz radiometers on the spills. Measurements using sorbents were used to calibrate the

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FIGURE 6.27 A rainbow sheen above another sunken vessel. The appearance of a rainbow sheen is the only strong visible indicator of slick thickness, and thickness may be between about 0.7 and 2.5 mm. Photography by Environment Canada.

radiometer. It was noted that the sheens typically had a thickness of 2 to 4 mm. It was found that 90% of the oil was in 10% of the slick area and that the microwave threshold was about 0.1 mm (100 mm). A series of experiments was carried out in 1979 to evaluate IR and SLAR for oil spill detection.154 The imagery was correlated against visual and sorbent measurements, which were used to derive a thickness estimate. It was concluded that the IR threshold was between 25 and 50 mm and for SLAR 100 nm. Furthermore, manipulation of data showed that a mass balance could be achieved if the thickness at which the IR showed oil to be colder at the sea occurred at 100 mm and for the heated portion of the oil at 1,000 mm. The United Kingdom conducted Isowake Experiments in 1982.155,156 On the basis of estimations and calculations, it was concluded that the lowest detectable slick thickness for IR was between 10 and 50 mm, whereas hot spots in the IR image could be as much as 1,000 mm. MacDonald et al. used photography from the space shuttle to define up to 124 slicks in an area of the Gulf of Mexico, offshore Louisiana.157 Similarly, a thematic image from Landsat showed at least 66 slicks in one large area. Some of the thickness relationships were based on unpublished experimental data from Duckworth. Brown et al. conducted experiments to measure the visibility of oil slicks. The observers and a visible UV camera were mounted in a crane basket 30 m over the slick.12,158,159 It was found that the detection ability decreased by over 50% for most oils and for the cameras when the angle was changed from 90 to 55 degrees from the horizontal (equivalent incidence angle of 0 to 35 degrees). Detectability degraded to 70% and sometimes to nil as the viewing angle was

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decreased past 55 through 35 degrees. Brown et al. conducted several experiments to ascertain the relationship between thickness of slicks and the density (or intensity) of the IR image.39 The thicknesses varied between 1 and 10 mm, and thicknesses were measured using an acoustic system. No relationship between slick thickness and IR brightness was found.

6.6.3. Specific Thickness Sensors The suppression of the water Raman peak in laser fluorosensor data has not been fully exploited or tested. This technique may work for thin slicks, but not necessarily for thick ones, at least not with a single excitation frequency. Attempts have been made to calibrate the thickness appearance of IR imagery, but also without success. It is suspected that the temperatures of the slick as seen in the IR are highly dependent on oil type, sun angle, and weather conditions. If so, it may not be possible to use IR as a calibrated tool for measuring thickness. Because accurate ground-truth methods do not exist, it is very difficult to calibrate existing equipment.160,161 The use of sorbent techniques to measure surface thickness yields highly variable results.151 As noted in the section on microwave radiometers, the signal strength measured by these instruments can imply one of several thicknesses. This methodology does not appear to have potential other than for measuring relative oil thickness. A variety of electrical, optical, and acoustic techniques for measuring oil thickness have been investigated.161,162 Two promising techniques were pursued in a series of laboratory measurements. In the first technique, known as thermal mapping, a laser is used to heat a region of oil, and the resultant temperature profiles created over a small region near this heating are examined using an IR camera.163 The temperature profiles created are dependent on the oil thickness. A more promising technique involves laser acoustics.164,165 The Laser Ultrasonic Remote Sensing of Oil Thickness (LURSOT) sensor consists of three lasers, one of which is coupled to an interferometer to accurately measure oil thickness.160,165-168 The sensing process is initiated with a thermal pulse created in the oil layer by the absorption of a powerful CO2 laser pulse. Rapid thermal expansion of the oil occurs near the surface where the laser beam was absorbed, which causes a steplike rise of the sample surface as well as an acoustic pulse of high frequency and large bandwidth (~15 MHz for oil). The acoustic pulse travels down through the oil until it reaches the oilewater interface where it is partially transmitted and partially reflected back toward the oileair interface, where it slightly displaces the oil’s surface. The time required for the acoustic pulse to travel through the oil and back to the surface again is a function of the thickness and the acoustic velocity of the oil. The displacement of the surface is measured by a second laser probe beam aimed at the surface. Motion of the surface induces a phase or frequency shift (Doppler shift) in the reflected probe beam. This phase or frequency modulation of the probe beam can then be demodulated with an interferometer.169 The thickness

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FIGURE 6.28 The signal from a 3-laser-thickness sensor. The time corresponds to a thickness of about 6 mm. This was measured by a prototype sensor mounted in an aircraft and flying over bins with various thicknesses of oil on water.

can be determined from the time of propagation of the acoustic wave between the upper and lower surfaces of the oil slick. This is a very reliable means of studying oil thickness and has great potential. Laboratory tests have confirmed the viability of the method, and a test unit has been flown to confirm its operability.160 Figure 6.28 shows the first airborne measurement of slick thickness. Several attempts have been made to measure thickness by using visible spectral imaging. As there are no visual indications other than the rainbow sheen area around 0.8 mm, these efforts are wasted.8,170

6.7. ACOUSTIC SYSTEMS Pogorzelski has shown that acoustic means can be used to measure oil viscosities on the surface.171 A directional acoustic system employing highfrequency forward specular scattering was used in the laboratory and at sea. Signals scattered are related to the rheological film properties. It is not known at this time if the system is scalable or exactly what the limitations are.

6.8. INTEGRATED AIRBORNE SENSOR SYSTEMS Increasingly, a number of different types of airborne oil spill remote sensors are being consolidated into sensor systems. The reason for this integration is to take advantage of the different information provided by each of the specific sensors and combine the information to provide a more complete and comprehensive

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information product. Although each of the individual sensors has specific inherent weaknesses such as false detections, these false detections are often different for each sensor type; hence a consolidation of information can help resolve and remove some of the uncertainties that exist from a single data source. Furthermore, additional information such as the relative thickness of the oil slick can be deduced from the overlaying of imagery from several sensor types. Although the absolute thickness of an oil slick remains the subject of continued research and scientific opinion, the ability to locate the thicker portions of the slick is essential in terms of operational spill cleanup and response. In addition to the integration of a number of remote sensors into a sensor system, information from other sources such as marine vessel traffic surveillance systems (i.e., automatic identification system, AIS) can be integrated and can play an essential role in identifying the source of the marine pollution. Two commercially available airborne marine oil spill remote-sensing systems are the MEDUSA and the MSS 6000.172-174 MEDUSA incorporates a number of sensor technologies such as laser fluorosensors, IR/UV line scanners, forward-looking IR sensors, microwave radiometers, SLAR systems, and camera systems, as well processing software into a flexible realtime data acquisition and processing system. The data from the various sensors are geo-referenced and fused with information from Airborne Information Systems (AIS) and marine surveillance radars into a GISdbased display output format. The processing software is known as the Oil Spill Scene Analysis System (OSSAS) and allows for the extraction of features such as the area of oil coverage, including areas of intermediate and thicker portions of the slick. The MSS 6000 Maritime Surveillance System is comprised of a flexible suite of sensors such as SLAR systems, IR/UV line scanners, forward-looking IR sensors, microwave radiometers, and camera systems, along with data processing and mission management software in order to perform the oil spill remote-sensing surveillance task. The MSS 6000 also focuses on sensor integration and includes AIS and marine search radar inputs. All sensor data, imagery, slick targets, vessels, and the like are annotated using navigation data from a single source to form an integrated part of a GIS). Both the MEDUSA and MSS 6000 can distribute their data in neardreal time via direct downlink or satellite communications to vessels or shore-based communications centers. A large number of maritime nations are now employing integrated airborne sensor systems.174,175

6.9. SATELLITE REMOTE SENSING The use of optical satellite remote sensing for oil spills has been attempted several times. The slick from the IXTOC I well blowout in Mexico was detected using GOES (Geostationary Operational Environmental Satellite) and by the AVHRR (Advanced Very High Resolution Radiometer) on the

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LANDSAT satellite.11 A blowout in the Persian Gulf was subsequently detected. The massive Exxon Valdez slick was detected on SPOT (Satellite Pour l’Observation de la Terre) satellite data.176 Oiled ice in Gabarus Bay resulting from the Kurdistan spill was detected using LANDSAT data.177,178 Several workers were able to detect the Arabian Gulf War Spill in 1991.179-182 The Haven spill near Italy was also monitored by satellite.183 A spill in the Barents Sea was tracked using an IR band on NOAA 10.184 It is significant to note that, in all these cases, the position of the oil was known and data had to be processed to actually see the oil, which usually took several weeks. Newer findings show that the ability to detect oil may be a complex function of conditions, oil types, and view angles.185-187 Figure 6.29 shows a visible satellite image of an area in Russia in which there was a massive pipeline spill. As noted in the caption for this image, the oil is not visible; however, a round lake in the image was mistaken for oil. Figure 6.30 shows an oiled ice area off Canada in which sediment and oil appear alike. There are several problems associated with relying on satellites operating in optical ranges, for oil spill remote sensing. The first is the timing and frequency of overpasses and the absolute need for clear skies to perform optical work.188 The chances of the overpass and the clear skies occurring at the same time give

FIGURE 6.29 A SPOT satellite visible image of an area in Russia where a large oil spill occurred. The oil spill is shown by the arrows. The black round objects are lakes. This illustrates that satellite visible imagery is difficult to interpret.

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FIGURE 6.30 A satellite visible image of an area off the east coast of Canada where an oil spill had occurred. Some of the black stripes in the white ice are oil mixed with sediment; others are sediment. The black on the right is land and the black on the left is sea. This image also illustrates the lack of discrimination of targets using visible satellite imagery.

a very low probability of seeing a spill on a satellite image. This point is well illustrated in the case of the Exxon Valdez spill.189 Although the spill covered vast amounts of ocean for over a month, there was only one clear day that coincided with a satellite overpass, and that was on April 7, 1989. Another disadvantage of satellite remote sensing is the difficulty in developing algorithms to highlight the oil slicks and the long time required to do so. For the Exxon Valdez spill, it took over two months before the first group managed to “see” the oil slick in the satellite imagery, although its location was precisely known. Recently, several workers have attempted to use MODIS visible data to detect oil spills.190,191 To be successful, these techniques generally rely on ancillary data such as suspected position or other satellite data. There is some information on slicks available from angular information. For example, Chust and Sagarminaga used the Multi-angle Imaging SpectroRadiometer (MISR) sensor aboard a satellite to detect oil spills on Lake Maracaibo, Venezuela.192 This sensor uses nine push-broom cameras at fixed angles from nadir to 70.5o to examine particular surfaces. A comparison of this angular sensor shows that better contrast was obtained than a simple nadir camera on another satellite. Data analysis showed that oil spills appear in

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greater contrast in those view angles affected by sun glitter because of the presence of oil. Recently, IR data from satellite has been used to map the land oil pollution in Kuwait.193 It was found that the old hydrocarbon-contaminated areas showed as much as 10oC difference from the surrounding land. Ground-truthing was used extensively in compiling the data. Casciello et al. also made an attempt to use IR imagery from the thermal IR region of the AVHRR satellite to locate known oil spills.194 Radar satellites, including ERS-1 and -2, Radarsat-1 and -2, and ENVISAT, are useful in detecting large offshore spills and in spotting anomalies.195-198 Radarsat has been used for detecting oil seeps and smaller spills resulting from an oil barge.199,200 The relative location of these smaller slicks was known before the detection. A novel application of Radarsat has been the study of oil lakes in the deserts of Kuwait.201,202 A number of nations now use radar satellites routinely to provide imagery for larger spills and to give indications of ship discharges. ERS-1 and 2 have been used for mapping oil spills in the Caspian Sea.202 Fortuny et al. describe the use of ERS-2 and ENVISAT to provide imagery during the Prestige incident off Spain.203 Torres Palenzuela and co workers used two ASAR (Advanced SAR) images from the Envisat satellite to study the Prestige spill off Spain.204 Using several techniques that were readily-available, such as filtering and comparison to GIS data of the areas, several slicks were identified. These slicks were confirmed by recorded sightings from helicopters and ships. Several countries have instituted satellite monitoring systems for oil pollution.205,206 Many of these countries use processing methods as described above. Extensive programs are in place in the Baltic Sea, North Sea, and English Channel.143 There are now beginning programs in the Black, Caspian, and Azov seas.206,207 Canada has had a program in place for several years.205 The Mediterranean Sea has had such a program for a long time.141,142 A constellation of monitoring satellites is proposed for the Mediterranean sea. In recent years, a number of new satellite-borne SAR sensors have been launched; see Table 6.2. While one of these sensors, Radarsat-2, operates in the traditional Cdband, TerraSARdX and Cosmos Skymed operate in the Xdband, while the PALSAR sensor on ALOS operates in the Ldband. As noted above, Xdband is the preferred band for oil spill remote sensing in terms of Bragg scattering. All four of these new SAR satellites have polarimetric imaging modes (some are experimental vs. operational modes) and much higher spatial resolution (down to 3 m), which may have application for oil spill remote sensing. Radarsat-2, like its predecessor, is an operational commercial satellite that can be tasked to respond to emergency situations such as major oil spills. The time required to task Radarsat-2 in emergency mode is now 4 hours, which is a large improvement from the 12 hours required to task its predecessor. As noted above, VV polarization provides a superior clutter-to-noise ratio (CNR) over HH polarization for oil spill detection. Radarsat-2 is fully

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TABLE 6.2 Current and Future Satellite-Borne SAR Sensors Satellite

Launch Date

Owner/Operator

Band

ERS-2

1995

European Space Agency

C

RADARSAT-1

1995

Canadian Space Agency

C

RADARSAT-2

2007

Canadian Space Agency

C

ENVISAT (ASAR)

2002

European Space Agency

C

ALOS (PALSAR)

2006

Japan Aerospace Exploration Agency

L

TerraSAR-X

2007

German Aerospace Centre

X

Tandem -X

tbd

German Aerospace Centre

X

Cosmos Skymed-1/2

2007

Italian Space Agency

X

TecSAR

2008

Israel Aerospace Industries

X

Sentinel-1

2012

European Space Agency

C

RADARSAT-Constellation (3-satellites)

2014

Canadian Space Agency

C

polarimetric, and there is interest in investigating whether a dual polarization ScanSAR mode utilizing VV/VH polarizations will work for oil and ship detection, respectively, as part of the Integrated Satellite Tracking of Pollution (ISTOP) program.208 The increased number of SAR satellites, as well as the plans to operate constellations of small satellites such as Cosmos (Constellation of Small Satellites for Mediterranean basin Observation), will provide increased temporal coverage with revisit times down to a few hours in some circumstances. The opportunity for increased frequency of image collection should prove useful to the oil spill response community. Figures 6.31 to 6.36 show the use of radar satellites and the look-alikes to oil that sometimes appear in the images.

6.10. OIL UNDER ICE DETECTION The difficulties in detecting oil in or under ice are numerous. Ice is never a homogeneous material but rather incorporates air, sediment, salt, and water, many of which may present false oil-in-ice signals to the detection mechanisms. In addition, snow on top of the ice or even incorporated into the ice adds complications. During freeze-up and thaw in the spring, there may not be distinct layers of water and ice. There are many different types of ice and

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FIGURE 6.31 A satellite radar image of a known spill off the Galapagos Islands. Note that most of the image consists of slick look-alikes.

different ice crystalline orientations. A separate subsection in this book provides a review of oil in and under ice.

6.11. UNDERWATER DETECTION AND TRACKING Many different techniques have been tried for underwater oil detection. First, the division should be made between oil in the water column or floating on a pycnocline, and oil on the bottom. Quite different physics and conditions can apply to these different situations. Several parties have tried to use standard sonars to detect submerged oil on the bottom. Oil on the bottom can appear as a softer surface than ordinary bottom sediment.209 The problem arises in that vegetation on the bottom also appears similar, and thus many false positives arise. In the water column, sonar can be useful as it can locate intermediate oil on pycnoclines; however, there is

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FIGURE 6.32 A satellite radar image of the large Sea Empress spill off the United Kingdom. The dark areas near the shore are calm areas. Note that the slick and these calm areas blend so that there is no delineation between them near the shoreline.

FIGURE 6.33 A Radarsat-1 image of the large Sea Empress spill off the United Kingdom. Similar features as in Figure 6.32 are noted.

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FIGURE 6.34 A third satellite radar image of the large Sea Empress spill off the United Kingdom. This image was taken a few days after the images in Figures 6.32 and 6.33. It appears that the oil slick has separated and some has moved to the bottom of the photograph and part toward the shore. This was never confirmed, and the “slick” at the bottom of the photograph may have indeed been oceanographic features.

no unique signature, and there are often weeds and other debris on pynoclines. Wendelboe et al. report on tests using a 200 and 400 kHz (dual-frequency) multibeam system.210 The contributing signal is the lower acoustic reflectivity of the oil than typical bottom geological formation or the better reflection than weed beds. Wendelboe et al. used the backscatter signals from several tests to develop algorithms for oil detection. This was tested in a tank with a 90% success rate and a 23% false detection rate. Oil on the bottom has successfully been mapped by underwater cameras, often mounted on sleds.210-213 The problems with this technique are the bottom visibilityd which is often insufficient to discriminatedand the difficulty in towing the camera vehicle as slow as 1 knot, the necessary speed. Pfeifer et al. were successful in employing mosaics of photographs to determine the aerial extent of oil on the seafloor.212-213 A low-technology approach had been historically employed. Heavy oil, oil such as would sink, often adheres to oil snares or pom-poms, which are polypropylene strips mounted much as a cheerleader’s pom-pom. These can be mounted on a beam and towed over the bottom and then raised periodically to see if oil has adhered.210 Alternatively, they can be mounted on an anchor with

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FIGURE 6.35 A radar image of the Nakhodka oil spill off the west coast of Japan (Radarsat-1). Only the black features on the lower half of the photograph and near the shoreline have been confirmed as oil. The remainder of the black areas are oceanographic features.

a marker buoy. These are then raised periodically to check whether the subsurface oil has contacted them. Camilli et al. have successfully applied mass spectrometry to the detection of sunken heavy oil (Fuel Oil #6).214 Using the small and enclosed mass spectrometer, TETHYS, the low-molecular-weight hydrocarbons coming from sunken oil masses are monitored. The mass spectrometer is mounted in a submersible that is driven over the seafloor. The exact position of the submersible is monitored closely using an acoustic positioning system on the surface. Signals then can be correlated closely to the position on the seafloor. Three ion peaks of m/z 43, 41, 27 are monitored to establish hydrocarbon

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FIGURE 6.36 A satellite radar image of an oil slick in the North Sea. This slick was confirmed by aerial observation.

presence. Tests show that the ion peaks provide sensitivity as low as 0.4 ppb. This is fully sufficient to monitor sunken oil. Tests were conducted in a test tank and later over actual spills in the Gulf of Mexico. The technique was able to find concentrations of sunken oil and place the locations within 1 meter. The tests in the Gulf of Mexico were conducted at depths of 200 meters and confirmed by using cameras on the submersible.

6.12. SMALL REMOTE-CONTROLLED AIRCRAFT Several parties have suggested using remote-controlled aircraft to provide more economical solutions for response personnel.215,216 In fact, remote-controlled aircraft have been used by a number of parties for monitoring a variety of pollutants since the 1970s.217 Belgium employs an Unmanned Aerial Vehicle (UAV) of the B-Hunter class to routinely monitor its portion of the North Sea.216 This is a large UAV that has visible and IR camera systems aboard. The unit has a 10-hour endurance over the targets. A variety of commercial platforms are now available that can carry small sensors such as visible and IR cameras. Furthermore, automatic navigation technology has now made these units, especially helicopters, very much easier to fly than in previous years.

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6.13. REAL-TIME DISPLAYS AND PRINTERS A very important aspect of remote sensing is the production of data so that operations people can quickly and directly use it. Real-time displays are important, so that remote sensor operators can adjust instruments directly in flight and provide information quickly on the location or state of the spill. A major concern of the client is that data be rapidly available.218 An additional concern is that the data from various sensors be available in a combined or fused form.85 Furthermore, there is a need to correct this data for aircraft motion and to annotate the data with time and position. At this time, existing hardware and software must be adapted as commercial off-the-shelf equipment for directly outputting and printing sensor data is not yet available. The displays and operators of a remote sensing aircraft are shown in Figure 6.37.

6.14. ROUTINE SURVEILLANCE One application of oil spill remote-sensing equipment is to detect and map slicks resulting from illegal discharges of oil from ships and offshore platforms. Historically, this task has always been performed using visual techniques, but in the past decade it has increasingly been turned over to aircraft with some

FIGURE 6.37 The interior of a remote sensing aircraft showing operators and displays. Photography by Environment Canada.

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instrumentation. Typical instrumentation includes a SLAR, IR/UV scanner, and cameras. This sensor package is economical compared to more ideal packages and greatly improves capability beyond just visual observation. Limitations include limited ability to ‘look into’ ship wakes, limited night operations, and inability to positively identify oil slicks. Recent additions such as improved SLAR systems, better display systems, and nighttime cameras have added to the capability but do not overcome these limitations. Figure 6.38 shows pilots overflying the stern of a ship to ascertain whether it is discharging oil. Many efforts have been made to perform surveillance of illegal discharges. Most existing operative remote systems are dedicated to this function. These are estimated to be around 35, most of these being around Europe.219 There are intensive programs in some areas, for example, in the North Sea. Carpenter reports on the 18-year program of surveillance in the North Sea.220 Some interesting statistics are noted. In 2004, 418 unidentified slicks were found, 65 slicks from oil rigs, and 57 slicks from ships. In 2004, 3,314 hours were flown in daylight and 594 in darkness. In the same year 91 slicks were found in the darkness and 449 in daylight. Ferraro et al. describe a routine surveillance program using satellite and aircraft data for the Mediterranean Sea.142 Future work in the Mediterranean Sea proposes a cluster of radar satellites to constantly monitor oil pollution.221 A word about aircraft is suitable here. A variety of aircraft are deployed as remote-sensing aircraft. Typically, different types are deployed for routine surveillance and for remote-sensing research. The latter requires flexibility in mounting sensors and in access to the outside of the aircraft. Figures 6.39 to 6.42 show some remote-sensing aircraft and highlight the modifications necessary.

FIGURE 6.38 A ship viewed from the cockpit of a remote sensing airplane. The ship will be overflown to ascertain whether it is discharging oil. Photography by Environment Canada.

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FIGURE 6.39 A view of a remote-sensing aircraft. The extensive airframe modifications are not visible in this photograph. The aircraft has extensive modifications in the interior to provide racks for equipment.

FIGURE 6.40 The bottom of the aircraft shown in Figure 6.39. This particular aircraft has 4 onemeter ports and one half-meter port. The direct opening in the one port is for a laser beam exit. Photography by Environment Canada.

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FIGURE 6.41 A view of another remote-sensing aircraft. This aircraft carries experimental radars, and one of the radomes is visible under the aircraft. Such modification can cost millions of dollars and take two years to complete.

6.15. FUTURE TRENDS Advances in sensor technology will continue to drive the use of remote sensors as operational oil spill response tools in the future. Cameras and thermal IR cameras that offer high sensitivity are cheap and plentiful. This improvement not only reduces the size and complexity of the sensor, but also the cost. In the next decade, advances in solid-state laser technology, in particular diodepumped solid-state lasers, will greatly reduce the size and energy consumption of laser-based remote sensors. This will promote the use of these sensors in smaller, more economical aircraft within the budget of many more regulatory agencies and maritime countries. Rapidly improving computer capabilities will allow for true real-time processing. At the present time and for the foreseeable future, there is no single “Magic Bullet” sensor that will provide all the information required to detect, classify, and quantify oil in the marine and coastal environment. An example of the improvement in recent years is that of the night-vision camera. It is now possible to use this sensor to visualize oil at night. An illustration of this appears in Figure 6.43. It will require the combined advances in sensor technologies and computer capabilities to gather, integrate, and merge several sources of data into a realtime format, usable by response crews in the field. If this type of information can be made available to response crews in a short enough timeframe following

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FIGURE 6.42 A view of the other side of the aircraft shown in Figure 6.41. This shows a lateral radome on the side of the aircraft as well as the radome on the underside. This aircraft also has extensive modifications in the interior to provide racks for equipment and power to sensors. Photography by Environment Canada.

a spill incident, then it can be used to lessen the potentially disastrous effects of a major oil spill on the marine ecosystem. As technology in remote-controlled systems evolve, it is possible to employ such technology in oil spill remote sensing. First efforts in the deployment of remote-controlled sensing aircraft have posted success and will, no doubt, be expanded in the future.222

6.16. RECOMMENDATIONS Recommendations are based on the above considerations and include economy as a major factor. Table 6.3 shows the considerations related to the development state, cost, and use of the sensor, and Table 6.4 shows the applicability of the sensor to various functions. The laser fluorosensor offers the only potential for discriminating between oiled and unoiled weeds or shoreline, and for positively identifying oil pollution on ice, among ice, and in a variety of other situations. This instrument, however, is large and expensive. A cheap sensor recommended for oil spill work is an IR camera. This is the cheapest undiscriminating device. This is the only piece of equipment that can be purchased off-the-shelf.

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FIGURE 6.43 A night-vision display. The annotation shows the various features of the image. Photography by Environment Canada.

All other sensors require special order and, often, development. Radar, though low in priority for purchase, offers the only potential for large area searches and foul weather remote sensing. Most other sensors are experimental or do not offer good potential for oil detection or mapping. Any sensor package should include a real-time printer and display, and a downlink. In order to respond effectively to major marine oil spills, a combination of airborne and satellite-borne sensor systems is recommended. Improvements in the resolution of satellite-based systems, particularly SAR systems combined with the increased number of such systems and the ability to steer them to image the area of the oil spill, will lead to their increased use in a tactical role. Being capable of imaging vast areas of the open ocean will ensure that satelliteborne sensors will also continue to be used in a strategic manner. There are a number of commercially available airborne sensor systems that provide near real-time information on oil slick location and indications of thicker areas of the pollution in an easily interpretable graphical manner. These airborne sensor systems are currently being employed by a large number of maritime nations in conjunction with satellite-based sensor systems. Historically, satellite sensors suffered from problems of low resolution and the low frequency of scene observation. These inadequacies are now being addressed by higher resolution systems with multiple imaging modes and the

156

TABLE 6.3 Attributes for Airborne Sensor Selection Specific to Oil

Immunity to False Targets

Typical Coverage (km)

Acquisition Cost Range k$

Aircraft Physical Requirements

Still Camera

High

High

Poor

Poor

0.25 to 2

1 to 5

no

Video

High

High

Poor

Poor

0.25 to 5

1 to 10

no

Night Time Vision Camera

Medium

Medium

Poor

Poor

0.25 to 2

5 to 20

no

IR Camera (8e14 mm)

High

Medium

Medium

Medium

0.25 to 2

20 to 50

no

UV Camera

Medium

Medium

Poor

Poor

0.25 to 2

4 to 20

no

MultiSpectral Scanner

Medium

Medium

Poor

Poor

0.25 to 2

100 to 200

Radar

High

High

Medium

Poor

5 to 50

1200 to 8000

yes-Dedicated

Microwave Radiometer

Medium

Medium

Medium

Medium

1 to 5

400 to 1000

yes-Dedicated

Laser Fluorosensor

Medium

Limited

Good

Good

0.01 to 0.1

300 to 1000

yes-Dedicated

some

Oil Analysis and Remote Sensing

Amount of Experience in Use

PART | III

Sensor

State of Development

Night & Fog Operation

Detection of Oil with Debris

Oiled Shoreline Survey

Spill Mapping

Ship Discharge Surveillance

Enforcement and Prosecution

Still Camera

2

n/a

1

2

2

2

2

Video

2

n/a

1

2

2

2

2

Night Time Vision Camera

3

4

1

n/a

2

2

2

IR Camera (8e14 mm)

4

2

1

n/a

3

3

3

UV Camera

2

n/a

n/a

n/a

3

2

1

UV/IR Scanner

4

2

1

n/a

4

3

3

Multi-Spectral Scanner

1

n/a

n/a

1

2

1

1

Radar

n/a

4

n/a

n/a

4

3

2

Microwave Radiometer

1

3

n/a

n/a

2

2

1

Laser Fluorosensor

4

3

5

5

1

5

5

Oil Spill Remote Sensing: A Review

Sensor

Support for Cleanup

Chapter | 6

TABLE 6.4 Sensor Suitability for Various Missions

Key: n/a ¼ not applicable; numerical values represent a scale from 1 ¼ poorly suited to 5 ¼ ideally suited

157

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ability to steer the sensor to look in the direction of the target of interest. There are an increasing number of satellite-borne SAR and optical sensors, some of which currently or soon will operate in constellations to provide increased coverage of the Earth’s surface. These enhanced capabilities will allow for the possible use of these sensors in a tactical mode of operation. In spite of these increased capabilities, there remains an essential role for airborne oil spill remote-sensing platforms. The ability to collect and deliver real-time oil slick location information will ensure the continued use of airborne systems in spite of their high operational costs. If this type of real-time oil spill remote-sensing information can be made available to response crews in a short enough timeframe following a spill incident, the information can be used to mitigate the potentially disastrous effects of a major oil spill on the marine ecosystem.

ACKNOWLEDGMENTS The authors acknowledge the many parties who contributed to this chapter, including the Canada Centre for Remote Sensing, and the Canadian Space Agency. In particular, they acknowledge Environment Canada for the many photographs in this chapter.

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29. Bolus RL. Airborne Testing of a Suite of Remote Sensors for Oil Spill Detecting on Water, Proceedings of the Second Thematic International Airborne Remote Sensing Conference and Exhibition, ERIM, 1996;III 743. 30. Goodman RH. Simple Remote Sensing System for the Detection of Oil on Water, Environmental Studies Research Fund Report Number 98, 1988. 31. Seakem Oceanography, Remote Sensing Chronic Oil Discharges, Environment Canada Report EE-108, 1988. 32. Kennicutt MC, MacDonald IR, Rogne T, Giammona C, Englehardt R. The Tenyo Maru Oil Spill: A Multi-Spectral and Sea Truth Experiment. AMOP 1992;349. 33. Rogne T, Macdonald I, Smith A, Kennicutt MC, Giammona C. Multi-Spectral Remote Sensing and Truth Data from the Tenyo Maru Oil Spill, Proceedings of the First Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM 37 [also published in Photogramm Eng Rem Sens, 1993, 1992;391. 34. Rogne TJ, Smith AM. Tenyo Maru Oil Spill Remote Sensing Data Analysis. Washington, D.C: Marine Spill Response Corporation; 1992. MSRC Technical Report Series 92-003. 35. Salisbury JW, D’Aria DM, Sabins FF. Thermal Infrared Remote Sensing of Crude Oil Slicks. Remote Sens Environ 1993;225. 36. Hover GL. Testing of Infrared Sensors for U.S. Coast Guard Oil Spill Response Applications, Proceedings of the Second Thematic Conference on Remote Sensing for Marine and Coastal Environments: Needs, Solutions and Applications, ERIM 1994;I-47. 37. Grierson IT. Use of Airborne Thermal Imagery to Detect and Monitor Inshore Oil Spill Residues During Darkness Hours. Environ Manage 1998;905. 38. Shih W-C, Andrews AB. Infrared Contrast of Crude-Oil-Covered Water Surfaces. Opt Lett 2008;3019. 39. Brown HM, Baschuk JJ, Goodman RH. The Limits of Visibility of Spilled Oil Sheens. AMOP 1998;805. 40. Brown CE, Fingas MF. Review of the Development of Laser Fluorosensors for Oil Spill Application. Mar Pollut Bull 2003;477. 41. Brown CE, Fruhwirth M, Wang Z, Lambert P, Fingas M. Airborne Oil Spill Sensor Test Program, Proceedings of the Second Thematic Conference on Remote Sensing for Marine and Coastal Environments: Needs, Solutions and Applications, ERIM, 1994;I-19. 42. Brown CE, Fingas MF, An J. Laser Fluorosensors: A Survey of Applications and Developments of a Versatile Sensor. AMOP 2001;485. 43. Brown CE, Nelson R, Fingas MF, Mullin JV. Airborne Laser Fluorosensing: Overflights During Lift Operations of a Sunken Oil Barge, Proceedings of the Fourth Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM 1997;I 23. 44. Brown CE, Marois R, Fingas MF, Choquet M, Monchalin J-P, Mullin J, et al. Airborne Oil Spill Sensor Testing: Progress and Recent Developments. IOSC 2001;917. 45. Brown CE, Fingas MF. Review of the Development of Laser Fluorosensors for Oil Spill Application. Mar Pollut Bull 2003;477. 46. Hengstermann T, Reuter R. Lidar Fluorosensing of Mineral Oil Spills on the Sea Surface. Appl Optics 1990;3218. 47. Balick L, DiBenedetto JA, Lutz SS. Fluorescence Emission Spectral Measurements for the Detection of Oil on Shore, Proceedings of the Fourth Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM 1997;I 13. 48. Sarma AK, Ryder AG. Comparison of the Fluoresence Behaviour of a Biocrude Oil and Crude Petroleum Oils. Energy & Fuels 2006;783.

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212. Pfeifer CE, Brzozowski R, Markian, Redman R. Quantifying Percent Cover of Submerged Oil Using Underwater Video Imagery. IOSC 2008;269. 213. Pfeifer CE, Brzozowski R, Markian, Redman R. Long-Term Monitoring of Submerged Oil in the Gulf of Mexico Following the T/B DBL 152 Incident. IOSC 2008;275. 214. Camilli R, Bingham B, Reddy CM, Nelson RK, Duryea AN. Method for Rapid Localization of Seafloor Petroleum Contamination Using Concurrent Mass Spectrometry and Acoustic Positioning. Mar Pollut Bull 2009;1505. 215. Lehr WJ. The Potential Use of Small UAS in Spill Response. IOSC 2008;431. 216. Donnay E. Use of Unmanned Aerial Vehicle (UAV) for the Detection and Surveillance of Marine Oil Spills in the Belgian Part of the North Sea. AMOP 2009;771. 217. Li K, Fingas MF, Pare´ JRP, Boileau P, Beaudry P, Dainty E. The Use of Remote-Controlled Helicopters for Air Sampling in an Emergency Response Situation, in. AMOP 1994;139. 218. Goodman RH. Overview and Future Trends in Oil Spill Remote Sensing. Spill Sci Techn 1994;11. 219. Huisman J. Use of Surveillance Technology to Support Response Decision Making and Impact Assessment. Interspill 2006. 220. Carpenter A. The Bonn Agreement Aerial Surveillance Programme: Trends in North Sea Oil Pollution: 1986e2004. Marine Pollution Bulletin 2007;149. 221. De Dominicis M, Pinardi N, Coppini G, Tonani M, Guarnieri A, et al. Interspill 2009. 222. Allen J, Walsh B. Enhanced Oil Spill Surveillance, Detection and Monitoring through the Applied Technology of Unmanned Air Systems. IOSC 2008;113.

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Chapter 7

Laser Fluorosensors Carl E. Brown

Chapter Outline 7.1. Principles of Operation 171 7.2. Oil Classification 175 7.3. Existing Operational 179 Units

7.4. Aircraft Requirements 7.5. Cost Estimates 7.6. Conclusions

180 182 182

7.1. PRINCIPLES OF OPERATION 7.1.1. Active versus Passive Sensors Passive sensors are those that measure naturally available energy such as that produced by the sun.1 Passive sensors can only be used when the naturally occurring energy is available, that is, during periods of daylight when the sun is illuminating the earth. Passive sensors cannot therefore be used during periods of darkness, which is at night. Some naturally emitted energy such as thermal infrared energy can be detected during the day or night, providing there is enough energy to be detected. Active sensors, on the other hand, provide their own energy or source of excitation for illumination. The sensor illuminates or excites the target to be investigated. The energy or radiation reflected from that target is then detected and measured by the active sensor. The main advantage of active sensors is the ability to obtain measurements anytime day or night. Furthermore, active sensors can be used at wavelengths that are not provided by the sun, such as the microwave region. It should be noted that active remote sensors require the generation of a large amount of energy in order to adequately illuminate the targets. Examples of active remote sensors include synthetic aperture radars (SARs) and laser fluorosensors.

7.1.2. Sensor Features Laser fluorosensors are the only sensors that detect a primary characteristic of oil, namely, the unique oil fluorescence spectral signature. Other generic sensors rely Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10007-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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on secondary characteristics of oil such as the reflection of light of various wavelengths, scattering of microwaves, and emission of infrared energy. Laser fluorosensors are active sensors that take advantage of the fact that certain compounds in petroleum oils absorb ultraviolet light and become electronically excited. This excitation is rapidly removed through the process of fluorescence emission, primarily in the visible region of the spectrum. Since very few other compounds show this tendency, fluorescence is a strong indication of the presence of oil. Natural fluorescing substances, such as chlorophyll, fluoresce at sufficiently different wavelengths than oil to avoid confusion. As different types of oil yield slightly different fluorescent intensities and spectral signatures, it is possible to differentiate between classes of oil under ideal conditions.2-10

7.1.2.1. Excitation Source and Wavelength Selection Most laser fluorosensors used for oil spill detection employ a laser excitation source operating in the ultraviolet region of 300 to 355 nm.4 The fluorescence response of crude oil when excited with an ultraviolet laser ranges from 400 to 650 nm, with peak centers in the 480 nm region. A typical laser fluorosensor system with excimer laser and scanner is shown in Figure 7.1. These excitation wavelengths are a compromise in that they excite all three classes of oil with reasonable efficiency (shorter wavelength lasers would excite lighter oils efficiently but would be rather poor at exciting crude and heavy refined oils). There FIGURE 7.1 Scanning Laser Environmental Airborne Fluorosensor (SLEAF). (Environment Canada)

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are several reasonably priced, commercially available ultraviolet lasers in the 300e355 nm region, including the XeCl excimer laser (308 nm), the nitrogen laser (337 nm), the XeF excimer laser at 351 nm, and the frequency-tripled Nd:YAG at 355 nm. With excitation in this wavelength region, there exists a broad organic matter fluorescent return, centered at 420 nm. This is referred to as Gelbstoff or yellow matter, which must be accounted for. While Gelbstoff disappears if the oil thickness is greater than 10e20 mm (i.e., where the oil is optically thick), it can be an interfering signal when attempting to detect thin films of light oils on water. Chlorophyll yields a sharp peak at 685 nm. Another phenomenon, known as Raman scattering, involves energy transfer between the incident light and the water molecules. When the incident ultraviolet light interacts with the water molecules, Raman scattering occurs. The water molecules absorb some of the energy as rotational vibrational energy and emit light at wavelengths that are the sum or difference between the incident radiation and the vibration rotational energy of the molecule. The Raman signal for water occurs at 344 nm when the incident wavelength is 308 nm (XeCl laser). The water Raman signal is useful for maintaining wavelength calibration of the fluorosensor in operation, but has also been used in a limited way to estimate oil thickness because the strong absorption by oil on the surface will suppress the water Raman signal in proportion to thickness,11 where transmittance ¼ EXP ( thickness absorption coefficient). The point at which the Raman signal is entirely suppressed depends on the type of oil, since each oil has a different absorption coefficient. The Raman signal suppression has led to estimates of sensor detection limits of about 0.05 to 0.1 mm.12 Details of the use of Raman scattering to measure oil slick thickness can be found in the early work of Hoge and Swift13 and the recent studies by Patsayeva et al.14

7.1.2.2. Detection System The detection systems in most laser fluorosensor systems usually involve the collection of laser-induced fluorescence by a telescope and the focusing of the fluorescence onto the entrance slit of a grating spectrometer and then onto either photomultiplier tubes or intensified diode-arrays. The fluorescence spectrum is then recorded at a number of selected wavelengths or over a wide spectral range covering the ultraviolet through the visible. 7.1.2.3. Range-Gating The majority of modern laser fluorosensors are equipped with range-gated detection systems. Range-gating is simply the turning on of the detection system at precisely the time at which the laser-induced fluorescence is expected to return to the laser fluorosensor. This is accomplished by turning the detection system on and off at a precise time based on the known altitude. To accomplish this, the timing of the laser pulse is monitored prior to exiting the aircraft and the elastic backscatter from the surface is then monitored to determine the

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precise aircraft altitude, which is then used to control the range-gating electronics. This allows the detector to observe only the fluorescence induced by the excitation laser and neglect the majority of the background solar radiation.

7.1.2.4. Field of ViewdFixed versus Scanning Systems As noted earlier, active sensors need to deliver sufficient energy to the target, the surface of the earth containing oil contamination, to excite sufficient fluorescence to allow for the detection and classification of the oil. With most airborne laser fluorosensor systems, this means illuminating a field of view (FOV) of about 1 3 mrad, giving a footprint on the surface of about 0.1 m 0.3 m at 100 m altitude. This does not allow for a large amount of the surface to be interrogated by each laser pulse. With higher-powered ultraviolet lasers, one can fly at higher altitudes and enlarge the footprint of the sensor. The repetition rate of the laser and the ground speed of the aircraft are also major factors in the sampling of the surface where the oil contamination is being examined. At ground speeds of 100e140 knots (nautical miles) at a laser repetition rate of 100 Hz, a fluorescence spectrum is collected approximately every 60 cm along the flight path (at 100 m altitude). Some laser fluorosensors only “look” directly below the aircraft and collect fluorescence spectra in a straight line; this is referred to as a “fixed” FOV system. As spilled oil often piles up in narrow bands at the high-tide line, detection of this oil with a fixed FOV system is not optimal. This means that the oil might not be detected because the sensor is striking the surface of the earth on either side of the high-tide line. To compensate for this tendency of the oil to accumulate in a narrow band, it is preferable to change the laser FOV by employing a scanner. The scanner can either be moved in a conical (circular) fashion or back and forth across the surface to increase the likelihood of striking the oil contamination. There are conical scanning laser fluorosensor systems that have been developed in Germany15 and Canada.16 An example of a conical scanner is shown in Figure 7.2.

7.1.3. Pros/Cons Laser fluorosensors are capable of detecting oil and related petroleum products in complex marine, coastal, and terrestrial environments. These sensors are extremely sensitive and can discriminate between oiled and unoiled naturally occurring substances such as kelp and seaweed. It is under these circumstances that the laser fluorosensor can aid in the direction of oil spill countermeasures by discriminating between contaminated and clean areas in the marine and terrestrial environment. Laser fluorosensors are the only sensors that detect a primary characteristic of oil, namely, the unique oil fluorescence spectral signature. Other generic sensors rely on secondary characteristics of oil such as the reflection of light of various wavelengths, scattering of microwaves, and emission of infrared energy.

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FIGURE 7.2 Scanner unit (narrow/wide swath) SLEAF system. (Environment Canada)

At the current time, laser fluorosensors are very large, heavy, and powerhungry systems. These characteristics necessitate the use of large multiengined aircraft to house the systems. These conditions will remain until much smaller diode-pumped solid-state lasers are developed in the ultraviolet region of the electomagnetic spectrum. This development has lagged behind that of solid-state lasers in the visible region and might be another decade in coming to fruition.

7.2. OIL CLASSIFICATION 7.2.1. Real-Time Analysis One of the benefits of modern laser fluorosensors is the ability to detect and classify oil contamination in real time. This availability of real-time oil contamination is essential for rapid oil spill response and environmental damage mitigation. A recent analysis of oil spill detection algorithms for laser fluorosensors has been undertaken by Jha et al.17 In earlier fluorosensors like the LEAF system, Pearson correlation coefficients were calculated to determine the presence of oil contamination and to broadly classify the petroleum products.18 Standard reference fluorescence spectra for light refined, crude, and heavy refined classes of oil, along with a standard water reference spectrum, were stored in the LEAF data analysis computer. Correlation coefficients were calculated for the live spectrum versus the three broad classes of petroleum products and water at the rate of 100 Hz. When the value of the correlation coefficient versus a class of petroleum product was above a certain level and greater than the correlation with the water spectrum, the live spectrum was identified as being of that class of petroleum.

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With modern computer technology, it is now possible to analyze a large amount of laser fluorosensor data in real time. For example, with the Scanning Laser Environmental Airborne Fluorosensor (SLEAF), it is possible to analyze fluorescence data at a rate of nearly 400 Hz and display oil detections along with the flight path of the aircraft on a geo-referenced map output.8,19 With the SLEAF system, fluorescence spectra are analyzed in real time to determine the presence or lack of oil in the sensor field-of-view. Principal component analysis20 is used to classify the oil class as light, medium, or heavy and to estimate the extent of oil coverage in the field of view as clean, light, moderate, or heavy.

7.2.2. Sensor Outputs As noted earlier, a high volume of fluorescence spectral data can be analyzed in real time. In most laser fluorosensor systems, the fluorescence data is georeferenced (i.e., the location of the oil contamination is well known) and the data can be presented in either spectral or map display outputs. While displays of spectral data are important for the sensor operator to verify the proper operation of the sensor, they are of little use to the spill responder. What is essential for the spill responder is knowledge of the location of the oil contamination so that spill response equipment can be rapidly deployed to the spill scene and cleanup operations undertaken. The positive identification of oil contamination afforded through the use of laser fluorosensors is one of their main advantages.

7.2.2.1. Spectral Data The display of spectral data is essential for the effective operation of modern laser fluorosensors. Laser fluorosensor display monitors often include a representation of sensor parameters such as laser pulse energy, operating altitude, laser backscatter energy, reference spectra, and live or real-time fluorescence spectra. The observation of each of these parameters is extremely useful to the sensor operator. In particular, real-time spectra are useful for providing an indication of the interaction of the laser beam with the surface. By observing the live spectrum, the operator has an indication of the water clarity through observation of the water Raman scattering spectrum. A trained sensor operator can easily recognize the presence of oil contamination by the characteristic spectrum of light refined, crude, or heavy refined petroleum products. Figure 7.3 shows typical laser-induced fluorescence spectra of a crude oil. The lack of a proper spectral signature can indicate a problem with the fluorosensor system such as low laser power, low laser backscatter signal, incorrect rangegate timing, or laser misalignment. It is impossible to display all of the spectral data collected with a high sampling rate laser fluorosensor system; a subsample of the spectral data is all that is needed for an experienced operator to monitor system operations.

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FIGURE 7.3 Laser-induced fluorescence spectradlight crude oildSLEAF system.

7.2.2.2. Map Display An efficient map display of oil contamination location(s) is essential for the rapid mitigation of the environmental effects of spilled oil. Displays of oil contamination superimposed over aircraft flight lines are useful for spill responders who participate in oil spill remote sensing overflights. Figure 7.4 shows the operator’s

FIGURE 7.4 Operator’s monitor for, sensor parameters, and map display of SLEAF system. (Environment Canada)

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FIGURE 7.5 Oil contamination indicated with colored bars perpendicular to aircraft flight path on SLEAF system map display. (Environment Canada)

display from Environment Canada’s SLEAF system. Figure 7.5 shows the operator’s display with areas of oil contamination illustrated as colored bars perpendicular to the flight path. Similar information is presented in Figure 7.6, which overlays the scanner pattern on the flight path along with the oil

FIGURE 7.6 Scanner pattern superimposed on aircraft flight path; colors are an indication of oil classification, SLEAF system. (Environment Canada)

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contamination information (different colors for clean, light, medium, or heavy oil classifications). Spill response organizations and personnel are not interested in sensor parameters or spectral data. What is needed are geo-referenced maps showing oil locations. These maps, or at least the geo-referenced oil contamination locations, should be in a format that can be transmitted electronically and be compatible with commonly used geographical information systems (GIS). These maps will help in the rapid and efficient deployment of spill response resources and equipment to the location(s) where oil contamination is the heaviest and the cleanup of contamination is most needed.

7.3. EXISTING OPERATIONAL UNITS 7.3.1. Airborne There are a number of operational airborne laser fluorosensor units operating around the globe. Some of these are research and development units that have progressed to become operational spill response sensors such as the SLEAF system.21 Other laser fluorosensors have been combined with other sensors in commercially available sensor packages, while others are standalone systems or remain as research instruments. There are a number of recent reviews of airborne laser fluorosensor systems in the literature, including those by Samberg,10 as well as Brown and Fingas.18, 22 There are a number of commercially available airborne laser fluorosensors in the marketplace and a few examples are provided here. The first is the Fluorescent LiDAR Spectrometer (FLS-AU) developed by Laser Diagnostics Instruments International Incorporated. The FLS series of LiDARs is designed for pollution monitoring of terrestrial, river, lake, and ocean targets, oil and gas pipeline leak detection, and oil exploration.23 The second example is the laser fluorosensor system developed by Optimare Sensorsysteme AG as part of the MEDUSA system.24 MEDUSA is a flexible real-time data acquisition and processing system that combines a number of sensor technologies for the detection, mapping, quantification, and classification of marine pollution. MEDUSA incorporates a number of unique sensor systems, for example, laser fluorosensors, infrared/ ultraviolet (IR/UV) line scanners, microwave radiometers, radar systems, camera systems, as well as the corresponding processing software. The final example is the Swedish Space Corporation’s MSS 6000 Maritime Surveillance System that can be tailored to integrate with a number of sensors, including a selection of side-looking airborne radar (SLAR), IR/UV cameras, microwave radiometers, forward-looking infrared (FLIR), and laser fluorosensors.25

7.3.2. Ship-Borne A small number of ship-borne laser fluorosensors have been developed. Most ship-borne laser fluorosensors are research and development technologies, although there have been recent commercial developments. Two examples of

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ship-borne laser fluorosensors are the FLIDAR (Fluorescence Lidar) developed by the research group at the Istituto di Ricerca sulle Onde Elettromagnetiche “Nello Carrarra” IROE-CNR26 and the compact lidar system developed by the Japanese Ship Research Institute.27 The FLIDAR system incorporates an XeCl excimer laser, a 12 spectral channel detection system, and a conical scanner to direct the ultraviolet laser beam onto the surface of the ocean alongside the marine vessel onto which the system is mounted. The compact lidar system at the Japanese Ship Research Institute is a frequency-tripled Nd:YAG laser coupled to an intensified CCD camera and uses a series of optical band-pass filters. One commercially available ship-borne laser fluorosensor system is the FLS-S (Fluorescence LiDAR SystemeShip-borne) developed by Laser Diagnostics Instruments International Incorporated. The FLS-S is designed to detect, measure, and map natural Dissolved Organic Matter (DOM), oil pollution, photosynthetic algae, and other contaminants in water.23

7.4. AIRCRAFT REQUIREMENTS The combination of large-size, heavyweight, and demanding power requirements for the ultraviolet lasers detailed below necessitate the use of midsized fixed-wing propeller or turboprop aircraft for laser fluorosensor system installation. Typical aircraft housing laser fluorosensors have included the Dornier 228-212 (see Figure 7.7), Douglas DC-3, CASA C-295, P-3B, and Beech B-99.18

7.4.1. Power The high-powered excimer lasers often employed in airborne laser fluorosensors for oil spill detection require a significant amount of power. This power is typically supplied in the form of 3-phase 208 VAC at 400 Hz for the excimer laser. Additional power is required for systems such as the laser

FIGURE 7.7 Dornier 228-212 twin turboprop aircraft.

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scanner head in the form of 28 VDC and for sensor-controller electronics at 220 VAC, 60 Hz. These power requirements necessitate the use of heavy-duty aircraft power generators and a number of power invertors to supply the energy in the proper voltage, phase, and frequency.

7.4.2. Weight Most laser fluorosensors for oil spill detection employ an ultraviolet excitation laser. In order to deliver enough laser pulse energy and a sufficient repetition rate for use in a fixed-wing aircraft, an excimer laser is usually required. Laser fluorosensor systems that employ excimer lasers such as the XeCl laser operating at 308 nm, capable of producing 150 mJ/pulse at a repetition rate of 400 Hz (such as that used in the SLEAF system shown in Figure 7.8),21 are large and heavy, weighing over 450 kg. Smaller systems are possible with the use of frequency-tripled Nd:YAG lasers operating at 355 nm; however, these lasers are unable to produce the high laser pulse energies and repetition rates to fly at higher altitudes with sufficient areal coverage. These systems are more suitable for helicopter-based platforms that can fly low and slowly and hover over a location for an extended period of time.

7.4.3. Operational Altitude Operational altitudes for laser fluorosensors are entirely dependent on the output energy of the excitation laser and field of view of the system optics. Typical operating altitudes for lasers operating in ultraviolet would be approximately 100 m for a system with 10 mJ/pulse of laser power up to

FIGURE 7.8 Lambda Physik Excimer LaserdSLEAF System. (Environment Canada)

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approximately 500 m for a laser output power of 150 mJ/pulse. The operational altitude will also determine the swath width achieved with a conical scanner; for details refer to Brown and Fingas.18

7.5. COST ESTIMATES The cost of a laser fluorosensor system is significant, partially due to the low production volume of these unique sensors. Cost estimates have been reported by Tebeau et al. for three laser fluorosensor systems ranging from $150,000 to $500,000 $US.28

7.6. CONCLUSIONS Laser fluorosensors capable of detecting oil and related petroleum products in complex marine, coastal, and terrestrial environments have been developed at a number of locations around the world. These sensors are extremely sensitive and can discriminate between oiled and unoiled naturally occurring substances such as kelp and seaweed. It is under these circumstances that the laser fluorosensor can aid in the direction of oil spill countermeasures by discriminating between contaminated and clean areas in the marine and terrestrial environment. Laser fluorosensors are the only sensors that detect a primary characteristic of oil, namely, the unique oil fluorescence spectral signature. Other generic sensors rely on secondary characteristics of oil such as the reflection of light of various wavelengths, scattering of microwaves, and emission of infrared energy. Advances in the fields of lasers, solid-state electronics, and computer operating hardware/software continue to fuel the development of advanced laser fluorosensors. While many of the current systems are large and require dedicated aircraft, the unique data sets available from these laser fluorosensors will ensure their continued development for years to come. Wide acceptance of laser fluorosensors as viable spill response tools will not be achieved until the size of the systems is reduced to a point where they can be flown routinely on small twin-engine aircraft. This reduction in size will come about when highpower, high-repetition rate, diode-pumped solid-state lasers are available.

REFERENCES 1. NRCan, http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter1/06_e.php, 2010. 2. Brown CE, Fingas MF, Gamble RL, Myslicki GE. The Remote Detection of Submerged Oil, In: Proc. Third R&D Forum on High-Density Oil Spill Response. International Maritime Organization; 2002;46. 3. Brown CE, Marois R, Myslicki G, Fingas MF. Initial Studies on the Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor. AMOP 2002;773. 4. Brown CE, Marois R, Myslicki G, Fingas MF, MacKay R. Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor. IOSC 2003;779.

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5. Brown CE, Marois R, Gamble RL, Fingas MF. Further Studies on the Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor. AMOP 2003;279. 6. Brown CE, Fingas MF, Marois R. Oil Spill Remote Sensing: Laser Fluorosensor Demonstration Flights off the East Coast of Canada. AMOP 2004;317. 7. Brown CE, Fingas M, Marois R, Fieldhouse B, Gamble RL. Remote Sensing of Water-in-Oil Emulsions: Initial Laser Fluorosensor Studies. AMOP 2004;295. 8. Brown CE, Fingas MF, Marois R. Oil Spill Remote Sensing Flights in the Coastal Waters Around Newfoundland. In: Proc. Eighth Intern. Conf. Remote Sensing for Marine and Coastal Environments. Ann Arbor, MI: Altarum; 2005. 9. Hengstermann T, Reuter R. Lidar Fluorosensing of Mineral Oil Spills on the Sea Surface. Appl Opt 1990;3218. 10. Samberg A. The State-of-the-Art of Airborne Laser Systems for Oil Mapping. Can J Rem Sens 2007;143. 11. Piskozub J, Drozdowska V, Varlamov V. A Lidar System for Remote Measurement of Oil Film Thickness on Sea Surface. In: Proc. Fourth Inter. Conf. Remote Sensing for Marine and Coastal Environments. Ann Arbor, MI: Environmental Research Institute of Michigan; 1997; 1:386. 12. Goodman R, Brown CE. Oil Detection Limits for a Number of Remote Sensing Systems. In: Proc. Eighth Inter. Conf. Remote Sensing for Marine and Coastal Environments. Ann Arbor, MI: Altarum Conferences; 2005. 13. Hoge FE, Swift RN. Oil Film Thickness Measurement Using Airborne Laser-Induced Water Raman Backscatter. Appl Opt 1980;3269. 14. Patsayeva S, Yuzhakov V, Varlamov V, Barbini R, Fantoni F, Frassanito C, et al. Laser Spectroscopy of Mineral Oils on Water Surface. EARSeL eProceedings; 2000;. 1:106. 15. Zielinski O, Andrews R, Go¨bel J, Hanslik M, Hunsa¨nger T, Reuter R. Operational Airborne Hydrographic Laser Fluorosensing. In: Proc. Fourth EARSel Workshop, Lidar Remote Sensing of Land and Sea. Dresden; 2001. 16. Brown CE, Marois R, Fingas MF. Preliminary Testing of the Scanning Laser Environmental Airborne Fluorosensor. AMOP 2000;519. 17. Jha NM, Gao Y, Levy J. An Analysis of Oil Spill Detection Algorithms Using Laser Fluorosensor Data. AMOP 2008;741. 18. Brown CE, Fingas MF. Review of the Development of Laser Fluorosensors for Oil Spill Application. Mar Pollut Bull 2003;477. 19. Brown CE, Fingas MF, Marois R. Oil Spill Remote Sensing Flights Around Vancouver Island. AMOP 2006;921. 20. James RTB, Dick R. Design of Algorithms for the Real-Time Airborne Detection of Littoral Oil Spills by Laser-Induced Fluorescence. AMOP 1996;1599. 21. Brown CE, Marois R. Laser Fluorosensor Demonstration Flights over Newfoundland Coastal Waters. AMOP 2007;437. 22. Brown CE, Fingas MF. The Latest Developments in Remote Sensing Technology for Oil Spill Detection. INTERSPILL; 2009. 23. LDI3, http://www.ldi3.com/index.php?main¼16, 2010. 24. Optimare, http://www.optimare.de/cms/en/divisions/fek.html, 2010. 25. Swedish Space Corporation, http://www.ssc.se/filearchive/7/7729/MSS%206000%20pamphlet %20.pdf, 2010. 26. Niccolai F, Bazzani M, Cecchi G, Innamorati M, Massi L, Nuccio C, Santoleri R. A Study for the Remote Monitoring of Organic Matter in the Ocean. In: Proc. EUROPTO Conf. Remote

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Sensing of the Ocean and the Sea Ice V, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications. SPIE 1999;3868:567. 27. Yamagashi S, Hitomi K, Yamanouchi H, Yamaguchi Y. Determination of a Lidar Signal from Images of Backscattered Natural Light on Water Surface. IOSC 2001;929. 28. Tebeau PA, Hansen KA, Fant JW, Terrien MM. Assessing the Long-term Implementation Costs versus Benefits Associated with Laser Fluorosensor Spill Response Technology. AMOP 2007;451.

Part IV

Behaviour of Oil in the Environment and Spill Modeling

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Chapter 8

Introduction to Spill Modeling Merv Fingas

Chapter Outline 8.1. Introduction 8.2. An Overview of Weathering

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8.1. INTRODUCTION When oil is spilled, whether on water or land, a number of transformation processes occur; many of these processes are referred to as the behavior of the oil. The first process is weathering, a series of processes whereby the physical and chemical properties of the oil change after the spill, of which the most important are evaporation and emulsification.1-4 A second group of processes are related to the movement of oil in the environment. Spill modeling combines the knowledge of both these sets of processes and provides the user with information on future locations of the oil as well as information on the state of the oil.5-8 Weathering and movement processes can overlap, with weathering strongly influencing how oil is moved in the environment and vice versa. All processes depend very much on the type of oil spilled and weather conditions during and after the spill.

8.2. AN OVERVIEW OF WEATHERING The specific behavior processes that occur after an oil spill determine how the oil should be cleaned up and its effect on the environment. For example, if an oil evaporates rapidly, cleanup is less intense, but the hydrocarbons in the oil enter the atmosphere. An oil slick could be carried by surface currents or winds to the vicinity of a bird colony or to a shore where seals or sea lions are breeding and severely affect the wildlife and their habitat. On the other hand, a slick could be carried out to sea where it disperses naturally and has less direct effect on the environment. Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10008-5 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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The fate and effects of a particular spill are determined by the behavior processes, which in turn are almost entirely determined by the type of oil and the environmental conditions at the time of the spill. Spill responders need to know the ultimate fate of the oil in order to take measures to minimize the overall impact of the spill. Oil spilled on water undergoes a series of changes in physical and chemical properties that in combination are termed weathering. Weathering processes occur at very different rates but begin immediately after oil is spilled into the environment. Weathering rates are not consistent throughout the duration of an oil spill and are usually highest immediately after the spill. Both weathering processes and the rates at which they occur depend more on the type of oil than on environmental conditions. Most weathering processes are highly temperature-dependent, however, and will often slow to insignificant rates as temperatures approach zero degrees. The processes included in weathering are evaporation, emulsification, natural dispersion, dissolution, photo-oxidation, sedimentation, adhesion to materials, interaction with mineral fines, biodegradation, and the formation of tarballs. These processes are listed in order of importance in terms of their effect on the percentage of total mass balance, that is, the greatest loss from the slick in terms of percentage, and what is known about the process.

8.2.1. Evaporation Evaporation is usually the most important weathering process.9 It has the greatest effect on the amount of oil remaining on water or land after a spill. Over a period of several days, a light fuel such as gasoline evaporates completely at typical ambient temperatures, whereas only a small percentage of a heavier Bunker C oil evaporates. The rate at which an oil evaporates depends primarily on the oil’s composition. Figure 8.1 shows the differential evaporation of several typical oils. The more volatile components an oil or fuel contains, the greater the extent and rate of its evaporation. Many components of heavier oils will not evaporate at all, even over long periods of time and at high temperatures. A separate section on evaporation appears in this section of the book. Oil and petroleum products evaporate in a slightly different manner than water, and the process is much less dependent on wind speed and surface area. Oil evaporation can be considerably slowed down, however, by the formation of a “crust” or “skin” on top of the oil. This happens primarily on land or in calm areas where the oil layer does not get mixed. The skin or crust is formed when the smaller compounds in the oil are removed, leaving the larger compounds, such as waxes and resins, at the surface. This crust then seals off the remainder of the oil and slows evaporation. Stranded oil from old spills has been reexamined over many years, and it has been found that when this crust has formed, there is no significant evaporation in the oil underneath. When this

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40

Time in Hours FIGURE 8.1 Oil evaporation curves for several typical oilsddata from experiments.

crust has not formed, a similar oil could be weathered to the hardness of wood over the same amount of years. The rate of evaporation is very rapid immediately after a spill and then slows considerably. About 80% of evaporation that does take place occurs in the first two days after a spill. The evaporation of most oils follows a logarithmic curve with time. Some oils such as diesel fuel, however, evaporate as the square root of time, at least for the first few days. This means that the evaporation rate slows very rapidly in both cases after a few days. This can be seen in Figure 8.1.

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The properties of an oil can change significantly with the extent of evaporation. If about 40% (by weight) of an oil evaporates, its viscosity could increase by as much as a thousandfold. Its density could rise by as much as 10% and its flash point by as much as 400%. The extent of evaporation can be the most important factor in determining properties of an oil at a given time after the spill and in changing the behavior of the oil.

8.2.2. Emulsification Emulsification is the process by which one liquid is dispersed into another one in the form of small droplets.10 Water droplets can remain in an oil layer in a stable form, and the resulting material is completely different. These waterin-oil emulsions are sometimes called mousse or chocolate mousse, as they resemble this dessert. In fact, both the actual version of chocolate mousse and butter are common examples of water-in-oil emulsions. The mechanism of emulsion formation is not yet fully understood, but it probably starts with sea energy forcing the entry of small water droplets, about 10 to 25 mm (or 0.010 to 0.025 mm) in size, into the oil. If the oil is only slightly viscous, these small droplets will not leave the oil quickly. On the other hand, if the oil is too viscous, droplets will not enter the oil to any significant extent. Once in the oil, the droplets slowly gravitate to the bottom of the oil layer. Asphaltenes and resins in the oil will interact with the water droplets to stabilize them. Depending on the quantity of asphaltenes and resins, as well as other conditions, an emulsion may be formed. The conditions required for emulsions of any stability to form may only be reached after a period of evaporation. Evaporation increases the viscosity to the critical value and increases the resin and asphaltene percentage in the oil. Further discussion on emulsion formation is found in a separate subsection following this introduction.10 Water can be present in oil in four ways. First, some oils contain about 1% water as soluble water. This water does not significantly change the physical or chemical properties of the oil. The second way is called entrainment, whereby water droplets are simply held in the oil by its viscosity to form an unstable emulsion. These are formed when water droplets are incorporated into oil by the sea’s wave action and there are not enough asphaltenes and resins in the oil. Unstable emulsions break down into water and oil within minutes or a few hours, at most, once the sea energy diminishes. The properties and appearance of the unstable emulsion are almost the same as those of the starting oil, although the water droplets may be large enough to be seen with the naked eye. Semi- or meso-stable emulsions represent the third way water can be present in oil. These emulsions are formed when the small droplets of water are stabilized to a certain extent by a combination of the viscosity of the oil and the interfacial action of asphaltenes and resins. These emulsions generally break down into oil and water or sometimes into water, oil, and stable emulsion within a few days. Meso-stable emulsions are viscous liquids that are reddish-brown in color.

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The fourth way that water exists in oil is in the form of stable emulsions. These form in a way similar to meso-stable emulsions except that the oil contains a sufficient amount of resins and asphaltenes to stabilize the water droplets. The viscosity of stable emulsions is 500 to 800 times higher than that of the starting oil, and the emulsion will remain stable for weeks and even months after formation. Stable emulsions are reddish-brown in color and appear to be nearly solid. Because of their high viscosity and near solidity, these emulsions do not spread and tend to remain in lumps or mats on the sea or shore. The formation of emulsions is an important event in an oil spill. First, and most importantly, it substantially increases the actual volume of the spill. Emulsions that contain about 70% water triple the volume of the oil spill. Even more significantly, the viscosity of the oil increases by as much as 1,000 times, depending on the type of emulsion formed. For example, an oil that has the viscosity of motor oil can triple in volume and become almost solid through the process of emulsification. These increases in volume and viscosity make cleanup operations more difficult. Emulsified oil is difficult or impossible to disperse, to recover with skimmers, or to burn. Emulsions can be broken down with special chemicals in order to recover the oil with skimmers or to burn it. It is thought that emulsions break down into oil and water by further weathering, oxidation, and freezethaw action. Meso–or semi-stable emulsions are relatively easy to break down, whereas stable emulsions may take months or years to break down naturally, if they ever do break down. Emulsion formation also changes the fate of the oil. It has been noted that when oil forms stable or meso-stable emulsions, evaporation slows considerably. Biodegradation also appears to slow down. The dissolution of soluble components from oil may also cease once emulsification has occurred.

8.2.3. Natural Dispersion Natural dispersion occurs when fine droplets of oil are transferred into the water column by wave action or turbulence. Small oil droplets (less than 20 mm or 0.020 mm) are relatively stable in water and will remain so for long periods of time. Large droplets tend to rise, and larger droplets (more than 50 mm) will not stay in the water column for more than a few seconds. Depending on oil conditions and the amount of sea energy available, natural dispersion can be insignificant or it can remove the bulk of the oil. In 1993, the oil from a stricken ship, the Braer, dispersed almost entirely as a result of high seas off Scotland at the time of the spill and the dispersible nature of the oil cargo.11 Natural dispersion is dependent on both the oil properties and the amount of sea energy.12 Heavy oils such as Bunker C or a heavy crude will not disperse naturally to any significant extent, whereas diesel fuel and even light crudes can disperse significantly if the saturate content is high and the asphaltene and resin

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contents are low. In addition, significant wave action is needed to disperse oil. In 40 years of monitoring spills on the oceans, those spills where oil has dispersed naturally have all occurred in very energetic seas. The long-term fate of dispersed oil is not known, although it may degrade to some extent as it consists primarily of saturate components. Some of the dispersed oil may also rise and form another surface slick, or it may become associated with sediment and be precipitated to the bottom.

8.2.4. Dissolution Through the process of dissolution, some of the soluble components of the oil are lost to the water under the slick.13,14 These include some of the lowermolecular-weight aromatics. As only a small amount, usually much less than a fraction of a percent of the oil, actually enters the water column, dissolution does not measurably change the mass balance of the oil. The significance of dissolution is that the soluble aromatic compounds are particularly toxic to fish and other aquatic life. If a spill of oil containing a large amount of soluble aromatic components occurs in shallow water and creates a high localized concentration of compounds, then significant numbers of aquatic organisms can be killed. Gasoline, diesel fuel, and light crude oils are the most likely to cause aquatic toxicity. A highly weathered oil is unlikely to dissolve into the water. On open water, the concentrations of hydrocarbons in the water column are unlikely to kill aquatic organisms. Dissolution occurs immediately after the spill occurs, and the rate of dissolution decreases rapidly after the spill as soluble substances are quickly depleted. Some of the soluble compounds also evaporate rapidly.

8.2.5. Photo-oxidation Photo-oxidation can change the composition of an oil.15-18 It occurs when the sun’s action on an oil slick causes oxygen and carbons to combine and form new products that may be resins. The resins may be somewhat soluble and dissolve into the water, or they may cause water-in-oil emulsions to form. It is not well understood how photo-oxidation specifically affects oils, although certain oils are susceptible to the process, whereas others are not. For most oils, photo-oxidation is not an important process in terms of changing their immediate fate or mass balance after a spill.

8.2.6. Sedimentation, Adhesion to Surfaces, and Oil-Fines Interaction Sedimentation is the process by which oil is deposited on the bottom of the sea or other water body. While the process itself is not well understood, certain

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facts about it are. Most sedimentation noted in the past has occurred when oil droplets reached a higher density than water after interacting with mineral matter in the water column. This interaction sometimes occurs on the shoreline or very close to the shore. Once oil is on the bottom, it is usually covered by other sediment and degrades very slowly. In a few well-studied spills, a significant amount (about 10%) of the oil was sedimented on the seafloor. Such amounts can be very harmful to biota that inevitably come in contact with the oil on the sea bottom. Because of the difficulty of studying sedimentation, data are limited. Oil is very adhesive, especially when it is moderately weathered, and binds to shoreline materials or other mineral material with which it comes in contact. A significant amount of oil can be left in the environment after a spill in the form of residual amounts adhering to shorelines and man-made structures such as piers and artificial shorelines. As this oil usually contains a high percentage of aromatics and asphaltenes with high molecular weight, it does not degrade significantly and can remain in the environment for decades. Oil slicks and oil on shorelines sometimes interact with mineral fines suspended in the water column, and the oil is thereby transferred to the water column.19 Particles of mineral with oil attached may be heavier than water and sink to the bottom as sediment or the oil may detach and refloat. Oil-fines interaction does not generally play a significant role in the fate of most oil spills in their early stages, but can have an impact on the rejuvenation of an oiled shoreline over the long term.

8.2.7. Biodegradation A large number of microorganisms are capable of degrading petroleum hydrocarbons. Many species of bacteria, fungi, and yeasts metabolize petroleum hydrocarbons as a food energy source.20,21 Bacteria and other degrading organisms are most abundant on land in areas where there have been petroleum seeps, although these microorganisms are found everywhere in the environment. As each species can utilize only a few related compounds at most, however, broad-spectrum degradation does not occur. Hydrocarbons metabolized by microorganisms are generally converted to an oxidized compound, which may be further degraded, may be soluble, or may accumulate in the remaining oil. The aquatic toxicity of the biodegradation products is sometimes greater than that of the parent compounds. The rate of biodegradation depends primarily on the nature of the hydrocarbons and then on the temperature. Generally, rates of degradation tend to increase as the temperature rises. Some groupings of bacteria, however, function better at lower temperatures, and others function better at higher temperatures. Indigenous bacteria and other microorganisms are often the best adapted and most effective at degrading oil as they are acclimatized to the temperatures and other conditions of the area. Adding “superbugs” to the oil does not necessarily improve the degradation rate.

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The rate of biodegradation is greatest on saturates, particularly those containing approximately 12 to 20 carbons. Aromatics and asphaltenes, which have a high molecular weight, biodegrade very slowly, if at all. This explains the durability of roof shingles containing tar and roads made of asphalt, as both tar and asphalt consist primarily of aromatics and asphaltenes. On the other hand, diesel fuel is a highly degradable product, for it is largely composed of degradable saturates and lower aromatics. Light crudes are also degradable to a degree. While gasoline contains degradable components, it also contains some compounds that are toxic to some microorganisms. These compounds generally evaporate more rapidly, but in almost all cases, most of the gasoline will evaporate before it can degrade. Heavy crudes contain little material that is readily degradable, and Bunker C contains almost none. The rate of biodegradation is also highly dependent on the availability of oxygen. On land, oils such as diesel can degrade rapidly at the surface, but very slowly if at all only a few centimeters below the surface, depending on oxygen availability. In water, oxygen levels can be so low that degradation is limited. It is estimated that it would take all the dissolved oxygen in approximately 400,000 L of seawater to completely degrade 1 L of oil. The rate of degradation also depends on the availability of nutrients such as nitrogen and phosphorus, which are most likely to be available on shorelines or on land. Finally, the rate of biodegradation also depends on the availability of the oil to the bacteria or microorganism. Oil degrades significantly at the oil-water interface at sea and, on land, mostly at the interface between soil and the oil. Biodegradation can be a very slow process for some oils. It may take weeks for 50% of a diesel fuel to biodegrade under optimal conditions and years for 10% of a crude oil to biodegrade under similar conditions. For this reason, biodegradation is not considered an important weathering process in the short term.

8.2.8. Sinking and Overwashing If oil is more dense than the surface water, it may sometimes actually sink. Some rare types of heavy crudes and Bunker C can reach these densities and sink. When this occurs, the oil may sink to a denser layer of water rather than to the bottom. Less dense layers of water may override these denser layers of water. This occurs, for example, when seas are not high and warmer fresh water from land overrides dense seawater. The fresh water has a density of about 1.00 g/mL and the sea water a density of about 1.03 g/mL. An oil with a density greater than 1.00 but less than 1.03 would sink through the layer of fresh water and ride on the layer of salt water. The layer of fresh water usually varies in depth from about 1 to 10 m. If the sea energy increases, the oil may actually reappear on the surface, as the density of the water increases from 1.00 to about 1.03.

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It is important to note that sinking of any form, whether to the bottom or to the top of a layer of dense sea water, is rare. When oil does sink, it complicates cleanup operations as the oil can be recovered only with underwater suction devices or special dredges. Overwashing is another phenomenon that occurs quite frequently and can hamper cleanup efforts. At moderate sea states, a dense slick can be overwashed with water. When this occurs, the oil can disappear from view, especially if the spill is being observed from an oblique angle, as would occur if someone is observing a slick from a ship. Overwashing causes confusion about the fate of an oil spill as it can give the impression that the oil has sunk and then resurfaced.

8.2.9. Formation of Tarballs Tarballs are agglomerations of thick oil less than about 10 cm in diameter. Larger accumulations of the same material ranging from about 10 cm to 1 m in diameter are called tar mats. Tar mats are pancake-shaped rather than round. Their formation is still not completely understood, but it is known that they are formed from the residuals of heavy crudes and Bunker C. After these oils weather at sea and slicks are broken up, the residuals remain in tarballs or tar mats. The re-formation of droplets into tarballs and tar mats has also been observed, with the binding force being simply adhesion. The formation of tarballs is the ultimate fate of many oils.19 These tarballs are then deposited on shorelines around the world. The oil may come from spills, but it is also residual oil from natural oil seeps or from deliberate operational releases such as from ships. Tarballs are regularly recovered by machine or by hand from recreational beaches. Figure 8.2 shows such a tarball on a beach.

FIGURE 8.2 Tarball on a beach. This is the fate of spills on the sea, if not cleaned up.

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8.3. MOVEMENT OF OIL AND OIL SPILL MODELING 8.3.1. Spreading

Area (square kilometres)

Oil spreads to a lesser extent and more slowly on land than on water. Oil spilled on or under ice spreads relatively rapidly but does not spread to as thin a slick as on water.21 On any surface other than water, such as ice or land, a large amount of oil is retained in depressions, cracks, and other surface irregularities. After an oil spill on water, the oil tends to spread into a slick over the water surface. This is especially true of the lighter products such as gasoline, diesel fuel, and light crude oils, which form very thin slicks. Heavier crudes and Bunker C initially spread to slicks several millimeters thick. Heavy oils may also form tarballs and tar mats and thus may not go through progressive stages of thinning. The area of spreading for different types of oil is illustrated in Figure 8.3. Oil spreads horizontally over the water surface even in the complete absence of wind and water currents. This spreading is caused by the force of gravity and the interfacial tension between oil and water. The viscosity of the oil opposes these forces. As time passes, the effect of gravity on the oil diminishes, but the force of the interfacial tension continues to spread the oil. The transition between these forces takes place in the first few hours after the spill occurs. As a general rule, an oil slick on water spreads relatively quickly immediately after a spill. The outer edges of a typical slick are usually thinner than the inside of the slick at this stage so that the slick may resemble a “fried egg.” After a day or so of spreading, this effect diminishes.

10

Crude oil Diesel

1

Bunker C

0.1 Gasoline

0.01 1

10

100

Time (minutes)

1000 1 day

10000 7 days

FIGURE 8.3 The spreading of several oils on the sea, less the amount evaporated. This is taken over a time period of one week. Gasoline initially spreads rapidly, and then its area shrinks because of rapid evaporation.

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Winds and currents also spread the oil out and speed up the process. Oil slicks will elongate in the direction of the wind and currents and, as spreading progresses, take on many shapes depending on the driving forces. Oil sheens often precede heavier or thicker oil concentrations. If the winds are high (more than 20 km/h), the sheen may separate from thicker slicks and move downwind. A slick often breaks into “windrows” on the sea under the influence of either waves or zones of convergence or divergence. Oil tends to concentrate between the crests of waves simply because of the force of gravity. There are often vertical circulation cells in the top 20 m of the sea. When two circulation cells meet, a zone of convergence is formed. When two currents diverge, it forms a zone of divergence. Oil moving along these zones is alternately concentrated and spread out by the circulation currents to form ribbons or windrows of oil rather than continuous slicks. In some locations close to shore, zones of convergence and divergence often occur in similar locations so that oil spills may appear to have similar trajectories and spreading behavior in these areas.

8.3.2. Movement of Oil Slicks In addition to their natural tendency to spread, oil slicks on water are moved along the water surface, primarily by surface currents and winds. If the oil slick is close to land and the wind speed is less than 10 km/h, the slick generally moves at a rate that is 100% of the surface current and approximately 3% of the wind speed. In this case, wind does not generally play an important role. If the wind is more than about 20 km/h, however, and the slick is on the open sea, wind predominates in determining the slick’s movement. Both the wind and surface current must be considered for most situations. The movement resulting from both wind and current inputs is illustrated in Figure 8.4. When attempting to determine the movement of an oil slick, two factors affect accuracy. The more significant factor is the inability to obtain accurate wind and current speeds at the time of a spill. The other, very minor factor is a phenomenon commonly known as the Coriolis effect, whereby the Earth’s rotation deflects a moving object slightly to the right in the northern hemisphere and to the left in the southern hemisphere.

3% of wind component

Resulting movement

Resulting movement

Current component

3% of wind component Current component

FIGURE 8.4 The effect of different wind and current directions on the resulting movement of an oil slick.

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8.3.3. Spill Modeling Spill response personnel need to know the direction in which an oil spill is moving in order to protect sensitive resources and coastline. To assist them with this, computerized mathematical models have been developed to predict the trajectory or pathway and fate of oil. Outputs of one such spill model are shown in Figure 8.5. Today’s sophisticated spill models combine the latest information on oil fate and behavior with computer technology to predict where the oil will go and what state it will be in when it gets there. Their major limitation to accurately predicting an oil slick’s movement is the lack of accurate estimates of water current and wind speeds along the predicted path. This is likely to remain a major limitation in the future. In addition to predicting the trajectory, these models can estimate the amount of evaporation, the possibility of emulsification, the amount of dissolution and the trajectory of the dissolved component, the amount and trajectory of the portion that is naturally dispersed, and the amount of oil deposited and remaining on shorelines. Accurate spill modeling is now a very important part of both contingency planning and actual spill response.

Spil source Current vectors

Net movement of slick

Path traveled by slick

Present location of oil parcels Scene 1 - About 10 hours after spill

Red shows shoreline hit

Scene 3 - About one day after spill

Scene 2 - About 10 hours after spill - with current vectors shown

Oil moves back into main channel from shoreline

Scene 4 - About three days after spill

FIGURE 8.5 Illustration of the outputs of a spill trajectory model.

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Spill models operate in a variety of modes. The most typical is the trajectory mode, which predicts the trajectory and weathering of the oil. The stochastic mode uses available data to predict a variety of scenarios for the oil spill, which includes the direction, fate, and property changes in the oil slick. In another mode, often called the receptor mode, a site on the shore or water is chosen and the trajectory from the source of the oil is calculated. Increasingly, statistically generated estimates are added to oil spill models to compensate for the lack of accurate knowledge of winds and currents.

REFERENCES 1. Boehm PD, Page DS, Brown JS, Neff JM, Bragg JR, Atlas RM. Distribution and Weathering of Crude Oil Residues on Shorelines 18 Years after the Exxon Valdez Spill. Env Sci Techn 2008;9210. 2. Wardlaw GD, Arey JS, Reddy CM, Nelson RK, Ventura GT, Valentine DL. Disentangling Oil Weathering at a Marine Seep Using GCxGC: Broad Metabolic Specificity Accompanies Subsurface Petroleum Biodegradation. Env Sci Techn 2008;7166. 3. Dı´ez S, Jover E, Bayona JM, Albaige´s J. Prestige Oil Spill. III. Fate of a Heavy Oil in the Marine Environment. Env Sci Techn 2007;3075. 4. Short JW, Irvine GV, Mann DH, Maselko JM, Pella JJ, et al. Slightly Weathered Exxon Valdez Oil Persists in Gulf of Alaska Beach Sediments after 16 Years. Env Sci Techn 2007;1245. 5. French-McCay DP. Modeling Impacts of Oil and Chemical Releases. Sea Techn 2006;21. 6. French-McCay DP. Modeling as a Scientific Tool in NRDA for Oil and Chemical Spills. IOSC; 2008. 7. Etkin DS, Michel J, McCay DF, Boufadel M, Li H. Development of a Practical Methodology for Integrating Shoreline Oil-Holding Capacity into Spill Modeling. AMOP 2008;564. 8. French-McCay D, Rowe J, Etkin DS. Transport and Impacts of Oil Spills in San Francisco BaydImplications for Response. AMOP 2008;159. 9. Fingas M. Evaporation Modeling, Chapter 9 in the present work; 2010. 10. Fingas M. Models for Water-in-Oil Emulsion Formation, Chapter 10 in the Present Work; 2010. 11. Lunel T. The Braer spill: Oil fate Governed by Dispersion. IOSC 2005;790. 12. Farwell C, Reddy CM, Peacock E, Nelson RK, Washburn L, Valentine DL. Weathering and the Fallout Plume of Heavy Oil from Strong Petroleum Seeps Near Coal Oil Point, CA. Env Sci Techn 2009;3542. 13. Danchuk S, Willson CS. Numerical Modeling of Oil Spills in the Inland Waterways of the Lower Mississippi River Delta. IOSC 2008;887. 14. Faksness L-G, Brandvik PJ. Distribution of Water Soluble Components from Arctic Marine Oil SpillsdA Combined Laboratory and Field Study. Cold Regions Sci Techn 2008;97. 15. Taghvaei Ganjali S, Nahri Niknafs B, Khosravi M. Photooxidation of Crude Petroleum Maltenic Fraction in Natural Simulated Conditions and Structural Elucidation of Photoproducts. Iranian J Env Health Sci Eng 2007;37. 16. Fernandez-Varela R, Gomez-Carracedo MP, Fresco-Rivera P, Andrade JM, Muniategui S, Prada D. Monitoring Photooxidation of the Prestige’s Oil Spill by Attenuated Total Reflectance Infrared Spectroscopy. Talanta 2006;409. 17. Plata DL, Sharpless CM, Reddy CM. Photochemical Degradation of Polycyclic Aromatic Hydrocarbons in Oil Films. Environ Sci Techn 2008;2432.

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18. Plata D, Reddy CM. Photochemical Degradation of Select Polycyclic Aromatic Hydrocarbons: First-Order Disappearance Rates and Primary Degradation Mechanisms in OilContaminated Coastal Zones. ACS Nat Meet Book of Abstr 2005;230. 19. Khelifa A, Gamble L. Prediction of Tar Ball Formation. AMOP 2006;79. 20. Lepo JE, Cripe CR, Kavanaugh JL, Zhang S, Norton GP. The Effect of Amount of Crude Oil on Extent of Its Biodegradation in Open waterdand Sandy BeachdLaboratory Simulations. Envir Techn 2003;1291. 21. Spaulding ML. A State-of-the-Art Review of Oil Spill Trajectory and Fate Modeling. Oil Chem Poll 1988;39.

Chapter 9

Evaporation Modeling Merv Fingas

Chapter Outline 9.1. Introduction 9.2. Review of Theoretical Concepts 9.3. Development of New Diffusion-Regulated Models 9.4. Complexities to the Diffusion-Regulated Model

201 205 212 229

9.5. Use of Evaporation Equations in Spill Models 9.6. Comparison of Model Approaches 9.7. Summary

233

235 240

9.1. INTRODUCTION Evaporation is a very important process for most oil spills. In a few days, typical crude oils can lose up to 40% of their volume.1 Most oil spill behavior models include evaporation as a process and in the output of the model. Despite the importance of the process, relatively little work has been conducted on the basic physics and chemistry of oil spill evaporation.2 The difficulty with studying oil evaporation is that oil is a mixture of hundreds of compounds and this mixture varies from source to source and even over time. Much of the work described in the literature focuses on “calibrating” equations developed for water evaporation.2 Furthermore, very little empirical data on oil evaporation was published until a decade ago. An important concept in understanding evaporation is to understand the mechanisms that regulate evaporation. If there were no regulation, evaporation would proceed nearly instantly. Figure 9.1 shows a schematic of the airboundary-layer regulated mechanism. The liquid would evaporate at a very high rate if it was not for the regulation caused by the slow transfer of vapor through the air boundary layer. The most common example of this type of regulation is applicable to water, and this concept enters into most people’s common view of evaporation. Evaporation of water can be increased by spreading it out or by increasing the wind speed. Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10009-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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Evaporation limited by diffusion rate into the air therefore air-boundary-layer regulated Air Air boundary layer

Liquid

FIGURE 9.1 Illustration of the air-boundary-layer regulation mechanism. The diffusion into the air layer is the limiting factor and serves to regulate the evaporation rate. This rate is affected by turbulence in the air, which will increase the transfer of the molecules across the boundary layer. This regulatory mechanism is true for pure liquids that have a high evaporation rate. Water is an example of such a liquid and is the most common concept held.

Many liquids are not air-boundary-layer regulated primarily because they evaporate too slowly to have the vapors saturate the air boundary layer above them. Many mixtures are often regulated by the diffusion of molecules inside the liquid to the surface of the liquid. This situation is illustrated in Figure 9.2. Such a mechanism is true for many slowly evaporating mixtures of compounds, such as oils and fuels. Some of the outcomes of this mechanism may seem counterintuitive to some people in that increasing the area may not increase the evaporation rate except to a small degree if the initial pool is very thick, such as over about 10 to 20 mm. Also, and perhaps more importantly, increasing wind speed does not increase evaporation. Evaporation limited by diffusion rate through liquid and through surface layer-therefore diffusion regulated Air Liquid surface

Liquid

FIGURE 9.2 Illustration of the diffusion-controlled regulation mechanism. The diffusion through the evaporating liquid is the limiting factor and thus the regulation mechanism. This mechanism is generally true for oils, fuels, and many other mixtures of liquids that both evaporate more slowly than water and are mixtures.

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Scientific and quantitative work on water evaporation is decades old.3,4 The basis for the oil evaporation work in the literature is water evaporation. There are several fundamental differences between the evaporation of a pure liquid such as water and that of a multicomponent system such as crude oil. Most obviously, the evaporation rate for a single-component liquid such as water is a constant with respect to time.3,4 Evaporative loss, by total weight or volume, is not linear with time for crude oils and other multicomponent fuel mixtures.5 Evaporation of a liquid can be considered as the movement of molecules from the surface into the vapor phase above it. The layer of air above the evaporation surface is known as the boundary layer.6 The characteristics of this air layer, or boundary layer, can influence evaporation. In the case of water, the boundary layer regulates the evaporation rate. Air can hold a variable amount of water, depending on temperature, as expressed by the relative humidity. Under conditions where the boundary layer is not moving (no wind) or has low turbulence, the air immediately above the water quickly becomes saturated and evaporation slows or ceases. In practice, the actual evaporation of water proceeds at a small fraction of the possible evaporation rate because of the saturation of the boundary layer. The air-boundary-layer physics are then said to regulate the evaporation of water. This regulation manifests itself in the sensitivity of evaporation to wind or turbulence. When turbulence is weak or absent, evaporation can slow down by orders-of-magnitude. The molecular diffusion of water molecules is at least 103 times slower than turbulent diffusion.6 Evaporation can be viewed as consisting of two components, fundamental evaporation and regulation mechanisms. Fundamental evaporation is that process consisting of the evaporation of the liquid directly into the vapor phase without regulation other than by the thermodynamics of the liquid itself. Regulation mechanisms are those processes that serve to regulate the final evaporation rate into the environment. For water, the main regulation factor is the air-boundary-layer regulation discussed above. Air-boundary-layer regulation is manifested by the limited rate of diffusion, both molecular and turbulent diffusion, and by saturation dynamics. Molecular diffusion is based on exchange of molecules over the mean-free path in the gas. The rate of molecular diffusion for water is about 105 slower than the maximum rate of evaporation possible, purely from thermodynamic considerations.6 The rate for turbulent diffusion, the combination of molecular diffusion and movement with turbulent air, is on the order of 102 slower than that for maximum evaporation. In fact, in the case of water, maximum evaporation is not known and has only been estimated by experiments in artificial environments or by calculation.3 If the evaporation of oil was like that of water and was air-boundary-layer regulated, one could write the mass-transfer rate in semi-empirical form (also in generic and unitless form) as: E K C Tu S

(1)

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where E is the evaporation rate in mass per unit area, K is the mass-transfer rate of the evaporating liquid, presumed constant for a given set of physical conditions, sometimes denoted as kg (gas phase mass-transfer coefficient, which may incorporate some of the other parameters noted here), C is the concentration (mass) of the evaporating fluid as a mass per volume, Tu is a factor characterizing the relative intensity of turbulence, and S is a factor that relates to the saturation of the boundary layer above the evaporating liquid. The saturation parameter, S, represents the effects of local advection on saturation dynamics. If the air is already saturated with the compound in question, the evaporation rate approaches zero. This also relates to the scale length of an evaporating pool. If one views a large pool over which a wind is blowing, there is a high probability that the air is saturated downwind and the evaporation rate per unit area is lower than for a smaller pool. It should be noted that there many equivalent ways of expressing this fundamental evaporation equation. Much of the pioneering work for evaporation studies was performed by Sutton who proposed an equation based largely on empirical work:7 E ¼ K Cs U

7=9

d 1=9 Scr

(2)

where Cs is the concentration of the evaporating fluid (mass/volume), U is the wind speed, d is the area of the pool, Sc is the Schmidt number, and r is the empirical exponent assigned values from 0 to 2/3. Other parameters are defined as above. The terms in this equation are analogous to the very generic equation, (1), proposed above. The turbulence is expressed by a combination of the wind speed, U, and the Schmidt number, Sc. The Schmidt number is the ratio of kinematic viscosity of air (v) to the molecular diffusivity (D) of the diffusing gas in air, that is, a dimensionless expression of the molecular diffusivity of the evaporating substance in air. The coefficient of the wind power typifies the turbulence level. The value of 0.78 (7/9) as chosen by Sutton, represents a turbulent wind, whereas a coefficient of 0.5 would represent a wind flow that was more laminar. The scale length is represented by d and has been given an empirical exponent of 1/9. For water, this represents a weak dependence on size. The exponent of the Schmidt number, r, represents the effect of the diffusivity of the particular chemical, and historically was assigned values between 0 and 2/3.7 This expression for water evaporation was subsequently used by those working on oil spills to predict and describe oil and petroleum evaporation. Much of the literature follows the work of Mackay.8,9 Mackay and co workers adapted the equations for hydrocarbons using the evaporation rate of cumene. Data on the evaporation of water and cumene have been used to correlate the gas phase mass-transfer coefficient as a function of wind speed and pool size by the equation: Km ¼ 0:0292 U 0:78 X 0:11 Sc0:67

(3)

Chapter | 9

Evaporation Modeling

205

where Km is the mass-transfer coefficient in units of mass per unit time and X is the pool diameter or the scale size of evaporating area. Stiver and Mackay subsequently developed this further by adding a second equation:9 N ¼ km AP=ðRTÞ

(4)

where N is the evaporative molar flux (mol/s), km is the mass-transfer coefficient at the prevailing wind (m/s), A is the area (m2), P is the vapor pressure of the bulk liquid (Pascals), R is the gas constant [8.314 Joules/(mol-K)], and T is the temperature (K ). Thus, boundary-layer regulation was assumed to be the primary regulation mechanism for oil and petroleum evaporation. This assumption was never well tested by experimentation, as revealed by a literature search.2 The implications of these assumptions are that evaporation rate for a given oil is increased by: l l l

increasing turbulence increasing wind speed increasing the surface area of a given mass of oil

These factors can then be verified experimentally to test whether oil is boundary-layer regulated.

9.2. REVIEW OF THEORETICAL CONCEPTS Blokker was the first to develop oil evaporation equations for oil evaporation at sea.10 His starting basis was theoretical. Oil was presumed to be a onecomponent liquid. The ASTM (American Society for Testing and Materials) distillation data and the average boiling points of successive fractions were used as the starting point to predict an overall vapor pressure. The average vapor pressure of these fractions was then calculated from the ClausiusClapeyron equation to yield: log

ps qM 1 1 ¼ p 4:57 T Ts

(5)

where p is the vapor pressure at the absolute temperature, T, ps is the vapor pressure at the boiling point, Ts (for ps, 760 mm Hg was used), q is the heat of evaporation in cal/g, and M is the molecular weight. The term qM/(4.57 Ts) was taken to be nearly constant for hydrocarbons (¼5.0 þ/ 0.2), and thus the expression was simplified to: log ps =p ¼ 5:0½ðTs TÞ=T

(6)

From the data obtained, the weathering curve was calculated, assuming that Raoult’s law is valid for this situation giving qM as a function of the percentage

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evaporated. Pasquill’s equation10 was applied stepwise, and the total evaporation time was obtained by summation: t ¼

DhDb X 1 Kev U a PM

(7)

where t is the total evaporation time in hours, Dh is the decrease in layer thickness in m, D is the diameter of the oil spill, b is a meteorological constant (assigned a value of 0.11), Kev is a constant for atmospheric stability (taken to be 1.2 108), a is a meteorological constant (assigned a value of 0.78), P is the vapor pressure at the absolute temperature, T, and M is the molecular weight of the component or oil mass. Thus the model is partially air-boundary-layer regulated. Blokker constructed a small wind tunnel and tested this equation against the evaporation of gasoline and a medium crude oil. The observed gasoline evaporation rate was much higher than was predicted, and the crude oil rate was much lower than predicted. The times of evaporation, however, were somewhat close, and the equation was accepted for further use. The above equations were then incorporated into spreading equations to yield equations to predict the simultaneous spreading and evaporation of oil and petroleum products. Mackay and Matsugu approached the problem by using the classical water evaporation and experimental work.8 The water evaporation equation was corrected to hydrocarbons using the evaporation rate of cumene. It was noted that the difference in constants was related to the enthalpy differences between water and cumene. Data on the evaporation of water and cumene were used to correlate the gas phase mass-transfer coefficient as a function of wind speed and pool size by the equation: Km ¼ 0:0292 U 0:78 X 0:11 Sc0:67

(8)

where Km is the mass-transfer coefficient in units of mass per unit time and X is the pool diameter or the scale size of evaporating area. Note that the exponent of the wind speed, U, is 0.78, which is equal to the classical water evaporation-derived coefficient. Mackay and Matsugu noted that for hydrocarbon mixtures, the evaporation process is more complex, being dependent on the liquid diffusion resistance being present. Experimental data on gasoline evaporation were compared with computed rates. The computed rates showed some agreement and suggested the presence of a liquid-phase masstransfer resistance. This work was subsequently extended by the same group to show that the evaporative loss of a mass of oil spilled can be estimated using a mass-transfer coefficient, as shown above.11 This approach was investigated with some laboratory data and tested against some known mass-transfer conditions on the sea. The conclusion was that this mass-transfer approach could result in predictions of evaporation at sea.

Chapter | 9

Evaporation Modeling

207

Butler developed a model to examine evaporation of specific hydrocarbon components.12 The weathering rate was taken as proportional to the equilibrium vapor pressure, P, of the compound and to the fraction remaining: dx=dt ¼ kPðx=xo Þ

(9)

where x is the amount of a particular component of a crude oil at time, t, xo is the amount of that same component present at the beginning of weathering (t ¼ 0), k is an empirical rate coefficient, and P is the vapor pressure of the oil component. Since petroleum is a complicated mixture of compounds, P is not equal to the vapor pressure of the pure compound, but neither would there be large variations in the activity coefficient as the weathering process occurs. For this reason, the activity coefficients were subsumed in the empirical rate coefficient k. P and k were taken as independent of the amount, x, for a fairly wide range of oils. The equation was then directly integrated to give the fraction of the original compound remaining after weathering as (similar to Henry’s law): x=xo ¼ expðktP=xo Þ

(10)

The vapor pressure of individual components was fit using a regression line to yield a predictor equation for vapor pressure: P ¼ expð10:94 1:06 NÞ

(11)

where P is the vapor pressure in Torr and N is the carbon number of the compound in question. This combined with Equation (10) yielded the following expression: x=xo ¼ exp½ðkt=xo Þexpð10:94 1:06 NÞ

(12)

where x/xo is the fraction of the component left after weathering, k is an empirical constant, xo is the original quantity of the component, and N is the carbon number of the component in question. Equation (12) predicts that the fraction weathered is a function of the carbon number and decreases at a rate that is faster than predicted from simple exponential decay. If the initial distribution of compounds is essentially uniform (xo independent of N ), then the above equation predicts that the carbon number where a constant fraction (e.g., half) of the initial amount has been lost (x ¼ 0.5 xo) is a logarithmic function of the time of weathering: N1=2 ¼ 10:66 þ 2:17 logðkt=xo Þ

(13)

where N1/2 is half the volume fraction of the oil. The equation was tested using data from some patches of oil on shoreline, whose age was known. The equation was able to predict the age of the samples relatively well. It was suggested that the equation was applicable to open water spills; however, this was never subsequently applied in models. It should be noted that this approach is somewhat similar to the model that will be described

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Behaviour of Oil in the Environment and Spill Modeling

later in this paper and also that it is not air-boundary-layer based but is a diffusion-like model based partially on empirical values. Yang and Wang developed an equation using the Mackay and Matsugu molecular diffusion process.13 The vapor phase mass-transfer process was expressed by: Die ¼ km ð pi piN Þ=½RTS

(14)

where Die is the vapor phase mass-transfer rate, km is a coefficient that lumps all the unknown factors that affect the value of Die, pi is the hydrocarbon vapor pressure of fraction, I, at the interface, piN is the hydrocarbon vapor pressure of fraction, I, at infinite altitude of the atmosphere, R is the universal gas constant, and Ts is the absolute temperature of the oil slick. The following functional relationship was proposed: km ¼ aAg eqU

(15)

where A is the slick area, U is the over-water wind speed, and a, q, and g are empirical coefficients. This functional relationship was based on the results of past investigations, including, for instance, those of MacKay and Matsugu who suggested the value of g to be in the range from 0.025 to 0.055.8 Further experiments were performed by Yang and Wang to determine the values of a and q.13 The results were found to be twofold. Experiments showed that a film formed on evaporating oils and that this film severely retarded evaporation. Before the surface film has developed (rt/ro < 1.0078): Kmb ¼ 69A0:0055 e0:42U

(16)

where Kmb is the coefficient that groups all factors affecting evaporation before the surface film has formed and A is the area. After the surface film has developed (rt/ro > 1.0078): Kma ¼ 1=5 kmb

(17)

where ro is initial oil density, rt is weathered oil density at time t, and Kma is the coefficient that groups all factors affecting evaporation after the surface film has formed. The evaporation rate was found to be reduced fivefold after the formation of the surface film. Brighton proposed that the standard formulation used by many workers required refining.14 E ¼ K Cs U 7=9 d1=9 Scr

(18)

His starting point for water evaporation was similar to that proposed by Sutton7 where E is the mean evaporation rate per unit area, K is an empirically determined constant, Cs is the concentration of the evaporation fluid (mass/volume), d is the area of the square or circular pool, and r is an empirical exponent assigned values from 0 to 2/3.

Chapter | 9

209

Evaporation Modeling

Brighton suggested that this equation does not conform to the basic dimensionless form involving the parameters U and Zo (wind speed and roughness length, respectively), which define the boundary-layer conditions. The key factor in Brighton’s analysis was to use a linear eddy-diffusivity profile. This feature implied that concentration profiles become logarithmic near the surface, which is suspected to be more realistic compared to the more finite values previously used. Using a power profile to provide an estimation of the turbulence, Brighton was able to substitute the following identities into the classical relationship: U ¼

u n k

(19)

where u* is the friction velocity, z1 is the reference height above the surface, z0 is the roughness length, and n is the power law dimensionless term. It should be noted that these are applicable to neutrally stable atmospheres. z1 (20) n ¼ In z0 The evaporation equation now becomes: z dX d ku zdX U ¼ z1 dx dz s dz

(21)

where z is the height above the surface, X is the concentration of the evaporating compounds, x is the dimension of the evaporating pool, k is given by K/u)z, and is the von Karman constant, and s is the turbulent Schmidt number (taken as 0.85). Brighton subsequently compared his model with several runs of experimental evaporation experiments in the field and in the laboratory. This included laboratory oil evaporation data.15 The model only correlated well with laboratory water evaporation data, and the reason given was that other data sets were “noisy.” The most frequently used work in spill modeling is that of Stiver and Mackay.9 It is based on some of the earlier work by Mackay and Matsugu, but significant additions were made.8 Additional information is given in a thesis by Stiver.16 The formulation was initiated with assumptions about the evaporation of a liquid. If a liquid is spilled, the rate of evaporation is given by: N ¼ KAP=ðRTÞ

(22)

where N is the evaporative molar flux (mol/s), K is the mass-transfer coefficient under the prevailing wind (ms1), A is the area (m2), and P is the vapor pressure of the bulk liquid. This equation was arranged to give: dFv =dt ¼ KAPv=ðVo RTÞ

(23)

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PART | IV

Behaviour of Oil in the Environment and Spill Modeling

where Fv is the volume fraction evaporated, v is the liquid’s molar volume (m3/mol), and Vo is the initial volume of spilled liquid (m3). By rearranging, we obtain:

or:

dFv ¼ ½Pv=ðRTÞðKAdt=Vo Þ

(24)

dFv ¼ Hdq

(25)

where H is Henry’s law constant and q is the evaporative exposure. The right-hand side of the second to last equation has been separated into two dimensionless groups. The group, KAdt/Vo, represents the time rate of what has been termed the “evaporative exposure” and was denoted as dq. The evaporative exposure is a function of time, the spill area and volume (or thickness), and the mass-transfer coefficient (which is dependent on the wind speed). The evaporative exposure can be viewed as the ratio of exposed vapor volume to the initial liquid volume. The group Pv/(RT) or H is a dimensionless Henry’s law constant or ratio of the equilibrium concentration of the substance in the vapor phase [P/(RT)] to that in the liquid (l/v). H is a function of temperature. The product qH is thus the ratio of the amount that has evaporated (oil concentration in vapor times vapor volume) to the amount originally present. For a pure liquid, H is independent of Fv and Equation 2.26 was integrated directly to give: Fv ¼ Hq

(26)

If K, A, and temperature are constant, the evaporation rate is constant and evaporation is complete (Fv is unity) when q achieves a value of 1/H. If the liquid is a mixture, H depends on Fv and the basic equation can only be integrated if H is expressed as a function of Fv; that is, the principal variable of vapor pressure is expressed as a function of composition. The evaporation rate slows as evaporation proceeds in such cases. Equation (26) was replaced with a new equation developed using data from evaporation experiments: Fv ¼ ðT=K1 Þ Inð1 þ K1 q=TÞ expðK2 K3 =TÞ

(27)

where Fv is the volume fraction evaporated and K1,2,3 are empirical constants. A value for K1 was obtained from the slope of the Fv versus log q curve from pan or bubble evaporation experiments. For q greater than 104, K1 was found to be approximately 2.3T divided by the slope. The expression exp(K2 K3/T) was then calculated, and K2 and K3 were determined individually from evaporation curves at two different temperatures. Variations of all the above equations have been used extensively by many other experimenters and for model application. Brown and Nicholson studied the weathering of a heavy oil, bitumen.17 They compared experimental data using a large-scale weathering tank with two

Chapter | 9

Evaporation Modeling

211

spill model outputs. In the FOOS model, the evaporative exposure concept is used in which the fraction of oil evaporated is given by a variant of the Mackay equation: F ¼ ½InðPÞ þ InðCEÞ þ 1=P=C

(28)

where F is the fraction evaporated, C is an empirical constant, and E is a measure of the evaporative exposure, defined as; E ¼ ðKm AvtÞ=ðRTVo Þ

(29)

Km ¼ 0:0048 U 0:78 Z 0:11 Sc0:67

(30)

where;

and where Km is the mass-transfer coefficient, A is the slick area, v is the oil molar volume, Vo is the initial slick volume, Z is the pool size scale factor, and Sc is the Schmidt number (taken as 2.7). Brown and Nicholson compared the measured evaporation for a 5 ms1 wind at an ambient temperature of 20 C, and evaluation was done with the equation above.17 A spill volume of 100 m3 was assumed. A value of about 105 m3/mol was used for the average molar volume. The model generally described the observed evaporation quite well, particularly during the first few hours. Later, however, the model consistently overpredicted the evaporation rate. A simple method of correcting the equation was implemented by assuming that the vapor phase Schmidt number decreases slightly as the skin on the oil thickens. In response, the evaporative exposure was modified to: Km ¼ ð0:0025 0:000021 tÞ U 0:78

(31)

The predicted evaporation then compared favorably with the measured values. Bobra conducted laboratory studies on the evaporation of crude oils.18 The evaporation curves for several crude oils and petroleum products were measured under several different environmental conditions. These data were compared to the equation developed by Stiver and Mackay.9 The equation used was: Fv ¼ In½1 þ BðTG =TÞ q expðA B To =TÞfT=BTG g

(32)

where FV is the fraction evaporated, TG is the gradient of the modified distillation curve, A and B are dimensionless constants, To is the initial boiling point of the oil, and q is the evaporative exposure as previously defined. The comparison by Bobra et al. showed that the Stiver and Mackay equation predicts the evaporation of most oils relatively well until time exceeds about 8 hours; after that it overpredicted the evaporation. The “overshoot” can be as much as 10% evaporative loss at the 24-hour mark. This is especially true for very light oils. The Stiver and Mackay equation was also found to underpredict or overpredict the evaporation of oils in the initial phases. In addition, it was found that the basic

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assumptions in the bubble experiment as proposed by early researchers, namely, that the air bubbles are saturated with vapor, was not correct.9 Bobra also noted that most oil evaporation follows a logarithmic curve with time and that a simple approach to this was much more accurate than using Equation (32).

9.3. DEVELOPMENT OF NEW DIFFUSION-REGULATED MODELS A review of the theoretical work above reveals that air-boundary-layer concepts are very limited and cannot accurately explain long-term evaporation. Fingas conducted a series of experiments over several years to examine the concepts further.19 The results of the boundary-regulation experiments are presented in the order of the experimental series.

9.3.1. Wind Experiments A major factor in determining whether or not oil evaporation is air-boundarylayer regulated is if the evaporation rate increases with wind as would be predicted by Equations (2) and (8) above. Experiments on the evaporation of oil with and without wind were conducted with ASMB (Alberta Sweet Mixed Blend), gasoline, and water. Water formed a baseline data set since much is known about its evaporation behavior.3 Regressions on the data were performed, and the equation parameters calculated. Curve coefficients are the constants from the best fit equation [Evap ¼ a ln(t)], t ¼ time in minutes, for logarithmic equations or Evap ¼ a Ot, for the square root equations. Oils with few components evaporating at one time have a tendency to fit square root curves.5 While data were calculated separately for percentage of weight lost and absolute weight, the latter is usually used because it is more convenient. The plots of wind speed versus the evaporation rate (as a percentage of weight lost) for each oil type are shown in Figures 9.3 to Figure 9.5. These figures show that the evaporation rates for oils and even the light product gasoline are not increased by a significant amount with increasing wind speed. In most cases, there is a small rise from the 0-wind level to the 1-m/s level, but after that, the rate remains relatively constant. The evaporation rate after the 0-wind value is nearly identical for all oils. This is due to the stirring effect on the oil, which increases the diffusion rate to the surface. The oil evaporation data can be compared to the evaporation of water, as illustrated in Figure 9.5. These data show the classical relationship of the water evaporation rate correlated with the wind speed (evaporation varies as U0.78, where U is wind speed). This, by itself, indicates that the oils studied here are not boundary-layer-regulated, but a small effect is seen in moving from 0-wind to 1 m/s, and not thereafter. This, as noted above, is due to the stirring effect of the wind thus increasing the diffusion rate.

Chapter | 9

213

Evaporation Modeling 50

Percent Evaporated

40

30

20 wind = 0 m/s wind = 1 m/s wind = 1.6 m/s wind = 2.1 m/s wind = 2.6 m/s

10

0

0

500

1000

1500

2000

2500

Time - Minutes FIGURE 9.3 Evaporation of ASMB with varying wind velocities. This figure shows that there is little variation with wind velocity except in going from the 0-wind-level up to the others. This is due to the stirring effect of wind and not air-boundary-layer regulation.

FIGURE 9.4 Evaporation of gasoline with varying wind velocities. This figure also shows that there is little variation with wind velocity except in going from the 0-wind-level up to the others. This again is due to the stirring effect of wind and not air-boundary layer regulation.

100

Percent Evaporated

80

60

40 wind = 0m/s wind =1 m/s wind = 1.6m/s wind = 2.1m/s wind = 2.6m/s

20

0

0

20

40

60

80 100 120 140 160 180

Time - Minutes

214

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

FIGURE 9.5 Evaporation of water with varying wind velocities. This figure shows dramatic differences in the evaporation rate of water with wind velocity. This is typical of air-boundary-layer regulation. Compare Figure 9.5 with oil evaporation in Figures 9.3 and 9.4, which do not show this trend of variance with wind velocity.

100

Percent Evaporated

80

60

40

20

wind = 0 m/s wind = 1 m/s wind = 1.6 m/s wind = 2.1 m/s wind = 2.6 m/s regression line

0 0

20

40

60

80

100 120 140 160 180

Time - Minutes

Figure 9.6 shows the rates of evaporation compared to the wind speed for all the liquids used in this study. This figure shows the evaporation rates of all test liquids versus wind speed. The lines shown are those calculated by linear regression. This clearly shows that water evaporation rate increased, as expected, with increasing wind velocity. The oils, ASMB, and gasoline, do not show any significant increase with increasing wind speed. In any case, they do not show the typical U0.78 relationship that water shows. All the above data show that oil is not boundary-layer-regulated. FIGURE 9.6 Correlation of evaporation rates and wind velocity. The lines are drawn through the data points from experimental values. This clearly shows no correlation of oil evaporation rates with wind velocity and the strong and expected high correlation of water with wind velocity. The water evaporation line is moved to fit on the vertical scale, but otherwise is unaltered.

Evaporation Rate (%/min. or %/ln min.)

25 Gasoline

20

15

10

Water

ASMB

5 FCC Heavy Cycle

0 0

1

Wind Velocity - m/s

2

Chapter | 9

215

Evaporation Modeling

9.3.2. Evaporation Rate and Area Air-boundary-layer regulated liquids evaporate much faster if one increases the area. A small spill of water on the kitchen cupboard can be evaporated quickly by spreading it out on the cupboard. A test of this tendency will also confirm the proposition that oil is diffusion regulated. ASMB was also used to conduct a series of experiments with varying evaporation area. The mass of the oil was kept constant so that the thickness of the oil would also vary. However, the greater the area, the less the thickness, and both factors would increase oil evaporation if it were boundary-layer regulated. The experiments show no correlation between area and evaporation rate. One can conclude that evaporation rate is not highly correlated with area, and thus the evaporation of oil is not air-boundary-layer regulated.

9.3.3. Study of Mass and Evaporation Rate Air-boundary-layer liquids show no correlation between the mass of oil evaporated and the evaporation rate; however, diffusion-regulated liquids do. ASMB oil was again used to conduct a series of experiments with volume as the major variant. Alternatively, thickness and area were held constant to ensure that the strict relationship between these two variables did not affect the final regression results. Figure 9.7 illustrates the relationship between evaporation rate and volume of evaporation material (also equivalent to mass of evaporating material). This figure illustrates a strong correlation between oil mass (or volume) and evaporation rate. This again proves that there is no boundary-layer regulation.

FIGURE 9.7 Correlation of oil mass with evaporation rate. The plots are the equation factors from the evaporation equation, which is approximately equivalent to evaporation rate. This shows a direct relationship between evaporation rate of oil and mass of oil. It indicates that oil is diffusion regulated.

Evaporation Rate - g/ln min.

5

4

3

2

1

0 0

20

40

60

80

Weight - Grams

100

120

140

216

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

9.3.4. Study of the Evaporation of Pure Hydrocarbonsdwith and Without Wind A study of the evaporation rate of pure hydrocarbons was conducted to test the classic boundary-layer evaporation theory as applied to the hydrocarbon constituents of oils. The evaporation rate data are illustrated in Figure 9.8. This figure shows that the evaporation rates of the pure hydrocarbons have a variable response to wind. Heptane (hydrocarbon number 7) shows a large difference between evaporation rate in wind and no wind conditions, indicating boundarylayer regulation. Decane (carbon number 10) shows a lesser effect, and dodecane (carbon number 12) shows a negligible difference between the two experimental conditions. This experiment shows the extent of boundary regulation and the reason for the small or negligible degree of boundary regulation shown by crude oils and petroleum products. Crude oil contains very little material with carbon numbers less than dodecane, often less than 3% of its composition. Even the more volatile petroleum products, gasoline and diesel fuel, only have limited amounts of compounds more volatile than decane, and thus are also not strongly boundary-layer regulated, if at all. 0.6

Evaporation Rate - g/min.

0.5

0.4

Wind

0.3

0.2

0.1

No Wind

0.0 6

8

10

12

14

16

Hydrocarbon Number FIGURE 9.8 Evaporation rate of pure hydrocarbons. This shows that hydrocarbons up to about C12 (dodecane) show some air-boundary-regulated behavior, whereas those above show no airboundary-regulated behavior. As most compounds in oil are higher than dodecane, the bulk oil would not show air-boundary-regulated behavior.

Chapter | 9

217

Evaporation Modeling

9.3.5. Other Factors Another evaluation of evaporation regulation is that of saturation concentration, the maximum concentration soluble in air. The saturation concentrations of water and several oil components are listed in Table 9.1.20 This table shows that saturation concentration of water is less than that of many common oil components. The saturation concentration of water is, in fact, about two orders of magnitude less than the saturation concentration of volatile oil components such as pentane. This further explains why even light oil components have little boundary-layer limitation. Further, the saturation concentration of water is so regulating that with a high relative humidity, there is little that can be added to the air.

9.3.6. Temperature Variation and Generic Equations Using Distillation Data Comparing the evaporation data with temperature, one finds that the evaporation equations for oils show some differences. The resulting finding that unique

TABLE 9.1 Saturation Concentration of Water and Hydrocarbons Substance

Saturation Concentration* in g/m3 at 25 C

water

20

n-pentane

1689

hexane

564

cyclohexane

357

benzene

319

n-heptane

196

methylcyclohexane

192

toluene

110

ethybenzene

40

p-xylene

38

m-xylene

35

o-xylene

29

*Values taken from Ullmann’s Encyclopedia20

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PART | IV

Behaviour of Oil in the Environment and Spill Modeling

equations may be needed for each oil is a significant disadvantage to practical end use, and a way to accurately predict evaporation using other readilyavailable data is necessary. Findings show that distillation data can be used to predict evaporation. Distillation data are very common and are often the only data used to characterize oils. This is because the data are crucial in operating refineries. Crudes are sometimes priced on the basis of their distillation data. New procedures to measure distillation data are very simple, fast, and repeatable. Further, it was noted that oils and fuels evaporated as two distinct typesd those that evaporated as a logarithm of time and those that evaporated as a square root of time.5 Most oils typically evaporated as a logarithm (natural) with time. Diesel fuel and similar oils, such as jet fuel, kerosene, and the like, evaporate as a square root of time. The reasons for this are simply that diesel fuel and the like have a narrower range of compounds that evaporate at similar yield rates, which are a summation of a square root.5 The empirically measured parameters at 15 C were correlated with both the slopes and the intercepts of the temperature equations. Full details of this correlation are given in the literature.22,23 For the variation with temperature, the resulting equation is: Percentage evaporated ¼ ½B þ 0:045ðT 15Þ InðtÞ

(33)

where B is the equation parameter at 15 C, T is temperature in degrees Celsius, and t is the time in minutes. Distillation data were directly correlated to the evaporation rates determined by experimentation. The distillation data used were the type that are stated as the temperature at which a fixed amount of material is lost. The optimal point, or point at which the regression coefficient is maximum, was found to be 180 C by using peak functions. The percent mass distilled at 180 degrees was used to calculate the relationship between the distillation values and the equation parameters. The equations used were derived from correlations of the data. The data from those oils that were better fitted with square root equationsddiesel, Bunker C light, and Fluid Catalytic Cracker (FCC) Heavy Cycledwere calculated separately. The equations derived from the regressions are as follows: For oils that follow a logarithmic equation: Percentage evaporated ¼ 0:165ð%DÞ InðtÞ

(34)

For oils that follow a square root equation: pﬃ Percentage evaporated ¼ 0:0254ð%DÞ t where %D is the percentage (by weight) distilled at 180 C.

(35)

Chapter | 9

219

Evaporation Modeling

These equations can be combined with the equations generated in previous work as shown in Equation (33) above to account for the temperature variations: For oils that follow a logarithmic equation: Percentage evaporated ¼ ½:165ð%DÞ þ :045ðT 15Þ lnðtÞ

(36)

For oils that follow a square root equation:

pﬃ Percentage evaporated ¼ ½:0254ð%DÞ þ :01ðT 15Þ t 180 C.

(37)

where %D is the percentage (by weight) distilled at In addition, a large number of experiments were performed on oils to directly measure their evaporation curves. The empirical equations that result are given in Table 9.2.

TABLE 9.2 Equations for Predicting Evaporation Oil Type

Equation

Adgo

%Ev ¼ (.11 þ .013T)/t

Adgo e long term

%Ev ¼ (.68 þ .045T)ln(t)

Alaminos Canyon Block 25

%Ev ¼ (2.01 þ .045T)ln(t)

Alaska North Slope (2002)

%Ev ¼ (2.86 þ .045T)ln(t)

Alberta Sweet Mixed Blend

%Ev ¼ (3.24 þ .054T)ln(t)

Amauligak

%Ev ¼ (1.63 þ .045T)ln(t)

Amauligak e f24

%Ev ¼ (1.91 þ .045T)ln(t)

Anadarko H1A-376

%Ev ¼ (2.66 þ .013T)/t

Arabian Medium

%Ev ¼ (1.89 þ .045T)ln(t)

Arabian Heavy

%Ev ¼ (1.31 þ .045T)ln(t)

Arabian Heavy

%Ev ¼ (2.71 þ .045T)ln(t)

Arabian Light

%Ev ¼ (2.52 þ .037T)ln(t)

Arabian Light

%Ev ¼ (3.41 þ .045T)ln(t)

Arabian Light (2001)

%Ev ¼ (2.4 þ .045T)ln(t)

ASMB e Standard #5

%Ev ¼ (3.35 þ .045T)ln(t)

ASMB (offshore)

%Ev ¼ (2.2 þ .045T)ln(t)

Av Gas 80

%Ev ¼ (15.4 þ .045T)ln(t)

(Continued )

220

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Avalon

%Ev ¼ (1.41 þ .045T)ln(t)

Avalon J-34

%Ev ¼ (1.58 þ .045T)ln(t)

Aviation Gasoline 100 LL

ln(%Ev) ¼ (0.5 þ .045T)ln(t)

Azeri e long term

%Ev ¼ (1.3 þ .045T)ln(t)

Azeri e short term

%Ev ¼ (0.09 þ .013T)/t

Barrow Island

%Ev ¼ (4.67 þ .045T)ln(t)

BCF-24

%Ev ¼ (1.08 þ .045T)ln(t)

Belridge Crude

%Ev ¼ (.01 þ .013T)/t

Bent Horn A-02

%Ev ¼ (3.19 þ .045T)ln(t)

Beta

%Ev ¼ (0.08 þ .013T)/t

Beta e long term

%Ev ¼ (0.29 þ .045T)ln(t)

Boscan

%Ev ¼ (0.15 þ .013T)/t

Brent

%Ev ¼ (3.39 þ .048T)ln(t)

Bunker C e Light (IFO~250)

%Ev ¼ (.0035 þ .0026T)/t

Bunker C e long term

%Ev ¼ (.21 þ .045T)ln(t)

Bunker C (2002)

%Ev ¼ (0.16 þ .013T)/t

Bunker C (short term)

%Ev ¼ (.35 þ .013T)/t

Bunker C Anchorage

%Ev ¼ (0.13 þ .013T)/t

Bunker C Anchorage (long term)

%Ev ¼ (0.31 þ .045T)ln(t)

California API 11

%Ev ¼ (0.13 þ .013T)/t

California API 15

%Ev ¼ (0.14 þ .013T)/t

Cano Limon

%Ev ¼ (1.71 þ .045T)ln(t)

Canola Oil

Litte

Carpenteria

%Ev ¼ (1.68 þ .045T)ln(t)

Cat Cracking Feed

%Ev ¼ (0.18 þ .013T)/t

Chavyo

%Ev ¼ (3.52 þ .045T)ln(t)

Cold Lake Bitumen

%Ev ¼ (0.16 þ .013T)/t

Combined Oil/Gas

%Ev ¼ (0.08 þ .013T)/t

Chapter | 9

221

Evaporation Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Compressor Lube Oil -New

%Ev ¼ (0.68 þ .045T)ln(t)

Cook Inlet e Granite Point

%Ev ¼ (4.54 þ .045T)ln(t)

Cook Inlet e Swanson River

%Ev ¼ (3.58 þ .045T)ln(t)

Cook Inlet New Batch

%Ev ¼ (3.1 þ .045T)ln(t)

Cook Inlet Trading Bay

%Ev ¼ (3.15 þ .045T)ln(t)

Corrosion Inhibitor Solvent

%Ev ¼ (0.02 þ .013T)/t

Crude Castor oil

Litte

Cusiana

%Ev ¼ (3.39 þ .045T)ln(t)

Delta West Block 97

%Ev ¼ (6.57 þ .045T)ln(t)

Diesel e Anchorage e long term

%Ev ¼ (4.54 þ .045T)ln(t)

Diesel e Anchorage e short term

%Ev ¼ (.51 þ .013T)/t

Diesel e long term

%Ev ¼ (5.8 þ .045T)ln(t)

Diesel Mobile1997

%Ev ¼ (0.03 þ .013T)/t

Diesel (2002)

%Ev ¼ (0.02 þ .013T)/t

Diesel (regular stock)

%Ev ¼ (.31 þ .018T)/t

Diesel fuel e Southern e long term

%Ev ¼ (2.18 þ .045T)ln(t)

Diesel fuel e Southern e short term

%Ev ¼ (0.02 þ .013T)/t

Diesel Fuel 2002

%Ev ¼ (5.91 þ .045T)ln(t)

Diesel Fuel 2002 short term

%Ev ¼ (0.39 þ .013T)/t

Diesel Mobile 1997 long term

%Ev ¼ (0.02 þ .013T)/t

Dos Cuadros

%Ev ¼ (1.88 þ .045T)ln(t)

Ekofisk

%Ev ¼ (4.92 þ .045T)ln(t)

Empire Crude

%Ev ¼ (2.21 þ .045T)ln(t)

Endicott

%Ev ¼ (0.9þ .045T)ln(t)

Esso Spartan EP-680 Industrial Oil

%Ev ¼ (0.66 þ .045T)ln(t)

Eugene Is. 224-condensate

%Ev ¼ (9.53 þ .045T)ln(t)

Eugene Island Block 32

%Ev ¼ (0.77þ .045T)ln(t)

(Continued )

222

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Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Eugene Island Block 43

%Ev ¼ (1.57 þ .045T)ln(t)

Evendell

%Ev ¼ (3.38 þ .045T)ln(t)

FCC Heavy cycle

%Ev ¼ (.17 þ .013T)/t

FCC Light

%Ev ¼ (0.17 þ .013T)/t

FCC Medium cycle

%Ev ¼ (.16 þ .013T)/t

FCC-VGO

%Ev ¼ (2.5 þ .013T)/t

Federated

%Ev ¼ (3.47 þ .045T)ln(t)

Federated (new-1999)

%Ev ¼ (3.45 þ .045T)ln(t)

Fuel Oil #5

%Ev ¼ (0.14 þ .013T)/t

Garden Banks 387

%Ev ¼ (1.84 þ .045T)ln(t)

Garden Banks 426

%Ev ¼ (3.44 þ .045T)ln(t)

Gasoline

%Ev ¼ (13.2 þ .21T)ln(t)

Genesis

%Ev ¼ (2.12 þ .045T)ln(t)

Green Canyon Block 109

%Ev ¼ (1.58 þ .045T)ln(t)

Green Canyon Block 184

%Ev ¼ (3.55 þ .045T)ln(t)

Green Canyon Block 200

%Ev ¼ (3.11 þ .045T)ln(t)

Green Canyon Block 65

%Ev ¼ (1.56þ .045T)ln(t)

Greenplus Hydraulic Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Greenplus Hydraulic Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Gulfaks

%Ev ¼ (2.29 þ .034T)ln(t)

Heavy Reformate

%Ev ¼ (0.17 þ .013T)/t

Hebron MD-4

%Ev ¼ (1.01 þ .045T)ln(t)

Heidrun

%Ev ¼ (1.95 þ .045T)ln(t)

Hibernia

%Ev ¼ (2.18 þ .045T)ln(t)

High Viscosity Fuel Oil

%Ev ¼ (0.12 þ .013T)/t

Hondo

%Ev ¼ (1.49 þ .045T)ln(t)

Hout

%Ev ¼ (2.29 þ .045T)ln(t)

IFO-180

%Ev ¼ (0.12 þ .013T)/t

Chapter | 9

223

Evaporation Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

IFO-30 (Svalbard)

%Ev ¼ (0.04 þ .045T)ln(t)

IFO-300 (old Bunker C)

%Ev ¼ (0.15 þ .013T)/t

Iranian Heavy

%Ev ¼ (2.27 þ .045T)ln(t)

Issungnak

%Ev ¼ (1.56 þ .045T)ln(t)

Isthmus

%Ev ¼ (2.48 þ .045T)ln(t)

Jet 40 Fuel

%Ev ¼ (8.96 þ .045T)ln(t)

Jet A1

%Ev ¼ (.59 þ .013T)/t

Jet Fuel (Anch)

%Ev ¼ (7.19 þ .045T)ln(t)

Jet Fuel (Anch) short term

%Ev ¼ (1.06 þ .013T)/t

Komineft

%Ev ¼ (2.73 þ .045T)ln(t)

Lago

%Ev ¼ (1.13 þ .045T)ln(t)

Lago Treco

%Ev ¼ (1.12 þ .045T)ln(t)

Lucula

%Ev ¼ (2.17 þ .045T)ln(t)

Main Pass Block 306

%Ev ¼ (2.86 þ .045T)ln(t)

Main Pass Block 37

%Ev ¼ (3.04 þ .045T)ln(t)

Malongo

%Ev ¼ (1.67 þ .045T)ln(t)

Marinus Turbine Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Marinus Valve Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Mars TLP

%Ev ¼ (2.18 þ .045T)ln(t)

Maui

%Ev ¼ (0.14 þ .013T)/t

Maya

%Ev ¼ (1.38 þ .045T)ln(t)

Mayan crude

%Ev ¼ (1.45 þ .045T)ln(t)

Mississipi Canyon Block 807

%Ev ¼ (2.28 þ .045T)ln(t)

Mississippi Canyon Bk. 72

%Ev ¼ (2.15 þ .045T)ln(t)

Mississippi Canyon Block 194

%Ev ¼ (2.62 þ .045T)ln(t)

Mississippi Canyon Block 807

%Ev ¼ (2.05 þ .045T)ln(t)

Morpeth

%Ev ¼ (1.58 þ .013T)/t

(Continued )

224

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Nektoralik

%Ev ¼ (0.62 þ .045T)ln(t)

Neptune Spar (Viosca Knoll 826)

%Ev ¼ (3.75 þ .045T)ln(t)

Nerlerk

%Ev ¼ (2.01 þ .045T)ln(t)

Ninian

%Ev ¼ (2.65 þ .045T)ln(t)

Norman Wells

%Ev ¼ (3.11 þ .045T)ln(t)

North Slope - Middle Pipeline

%Ev ¼ (2.64 þ .045T)ln(t)

North Slope - Northern Pipeline

%Ev ¼ (2.64 þ .045T)ln(t)

North Slope - Southern Pipeline

%Ev ¼ (2.47 þ .045T)ln(t)

Nugini

%Ev ¼ (1.64 þ .045T)ln(t)

Odoptu

%Ev ¼ (4.27 þ .045T)ln(t)

Olive Oil

Litte

Oriente

%Ev ¼ (1.32 þ .045T)ln(t)

Oriente

%Ev ¼ (1.57 þ .045T)ln(t)

Orimulsion 400 e dewatered

%Ev ¼ (3.6)ln(t) (at 15oC)

Orimulsion plus water

%Ev ¼ (3 þ .045T)ln(t)

Oseberg

%Ev ¼ (2.68 þ .045T)ln(t)

Panuke

%Ev ¼ (7.12 þ .045T)ln(t)

Petronius VK981A

%Ev ¼ (2.27 þ .013T)/t

Pitas Point

%Ev ¼ (7.04 þ .045T)ln(t)

Platform Gail (Sockeye)

%Ev ¼ (1.68 þ .045T)ln(t)

Platform Holly

%Ev ¼ (1.09 þ .045T)ln(t)

Platform Irene e long term

%Ev ¼ (0.74 þ .045T)ln(t)

Platform Irene-short term

%Ev ¼ (0.05 þ .013T)/t

Point Arguello-comingled

%Ev ¼ (1.43 þ .045T)ln(t)

Point Arguello Heavy

%Ev ¼ (0.94 þ .045T)ln(t)

Point Arguello Light

%Ev ¼ (2.44 þ .045T)ln(t)

Point Arguello Light e b

%Ev ¼ (2.3 þ .045T)ln(t)

Chapter | 9

225

Evaporation Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Polypropylene Tetramer

%Ev ¼ (0.25)(t) (at 15oC)

Port Hueneme

%Ev ¼ (0.3 þ .045T)ln(t)

Prudhoe Bay e old stock

%Ev ¼ (1.69 þ .045T)ln(t)

Prudhoe Bay (new stock)

%Ev ¼ (2.37 þ .045T)ln(t)

Prudhoe stock b

%Ev ¼ (1.4 þ .045T)ln(t)

Rangely

%Ev ¼ (1.89 þ .045T)ln(t)

Sahara Blend

%Ev ¼ (0.001 þ .013T)/t

Sahara Blend (long term)

%Ev ¼ (1.09 þ .045T)ln(t)

Sakalin

%Ev ¼ (4.16 þ .045T)ln(t)

Santa Clara

%Ev ¼ (1.63 þ .045T)ln(t)

Scotia Light

%Ev ¼ (6.87 þ .045T)ln(t)

Scotia Light

%Ev ¼ (6.92 þ .045T)ln(t)

Ship Shoal Block 239

%Ev ¼ (2.71 þ .045T)ln(t)

Ship Shoal Block 269

%Ev ¼ (3.37 þ .045T)ln(t)

Sockeye

%Ev ¼ (2.14 þ .045T)ln(t)

Sockeye (2001)

%Ev ¼ (1.52 þ .045T)ln(t)

Sockeye Comingled

%Ev ¼ (1.38 þ .045T)ln(t)

Sockeye Sour

%Ev ¼ (1.32 þ .045T)ln(t)

Sockeye Sweet

%Ev ¼ (2.39 þ .045T)ln(t)

South Louisiana

%Ev ¼ (2.39 þ .045T)ln(t)

South Louisiana (2001)

%Ev ¼ (2.74 þ .045T)ln(t)

South Pass Block 60

%Ev ¼ (2.91 þ .045T)ln(t)

South Pass Block 67

%Ev ¼ (2.17 þ .045T)ln(t)

South Pass Block 93

%Ev ¼ (1.5 þ .045T)ln(t)

South Timbalier Block 130

%Ev ¼ (2.77 þ .045T)ln(t)

Soybeam oil

Litte

Statfjord

%Ev ¼ (2.67 þ .06T)ln(t)

(Continued )

226

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Sumatran Heavy

%Ev ¼ (0.11 þ .013T)/t

Sumatran Light

%Ev ¼ (0.96 þ .045T)ln(t)

Taching

%Ev ¼ (0.11 þ .013T)/t

Takula

%Ev ¼ (1.95 þ .045T)ln(t)

Tapis

%Ev ¼ (3.04 þ .045T)ln(t)

Tchatamba Crude

%Ev ¼ (3.8 þ .045T)ln(t)

Terra Nova

%Ev ¼ (1.36 þ .045T)ln(t)

Terresso 150

%Ev ¼ (0.68 þ .045T)ln(t)

Terresso 220

%Ev ¼ (0.66 þ .045T)ln(t)

Terresso 46 Industrial oil

%Ev ¼ (0.67 þ .045T)ln(t)

Thevenard Island

%Ev ¼ (5.74 þ .045T)ln(t)

Troll

%Ev ¼ (2.26 þ .045T)ln(t)

Turbine Oil STO 90

%Ev ¼ (0.68 þ .045T)ln(t)

Turbine Oil STO 120

%Ev ¼ (0.68 þ .045T)ln(t)

Udang

%Ev ¼ (0.14 þ .013T)/t

Udang (long term)

%Ev ¼ (0.06 þ .045T)ln(t)

Vasconia

%Ev ¼ (0.84 þ .045T)ln(t)

Viosca Knoll Block 826

%Ev ¼ (2.04 þ .045T)ln(t)

Viosca Knoll Block 990

%Ev ¼ (3.16 þ .045T)ln(t)

Voltesso 35

%Ev ¼ (0.18 þ .013T)/t

Waxy Light and Heavy

%Ev ¼ (1.52 þ .045T)ln(t)

West Delta Block 143

%Ev ¼ (2.18 þ .045T)ln(t)

West Delta Block 30 w/water

%Ev ¼ (0.04 þ .013T)/t

West Texas Intermediate

%Ev ¼ (2.77 þ .045T)ln(t)

West Texas Intermediate

%Ev ¼ (3.08 þ .045T)ln(t)

West Texas Sour

%Ev ¼ (2.57 þ .045T)ln(t)

White Rose

%Ev ¼ (1.44 þ .045T)ln(t)

Zaire

%Ev ¼ (1.36 þ .045T)ln(t)

Chapter | 9

227

Evaporation Modeling

70

Percent Evaporated

60

Diesel at 20 degrees

50

Diesel at 5 degrees

40

North Slope at 20 degrees

30 20

North Slope at 5 degrees

10 0

0

20

40

60

80

100

120

Time in Hours FIGURE 9.9 Comparison of evaporation curves for diesel and Alaska North Slope oils at two different temperatures.

Since the equations described above require only time and temperature (or at the very worst, the percentage of oil distilled at 180 C), it is relatively simple to apply these forms of equations. They can also be applied in models as increments where t, the time, becomes the total time and the previous evaporation is subtracted. For example, if one was modeling the evaporation of ASMB oil in the time step from 12 to 15 hours. The equation is (from Table 9.2): Percentage evaporation ¼ ð3:24 þ :054TÞ InðtÞ

(38)

Substituting for the temperature of 15 C and with a time of 12 hours or 720 minutes, we get a percentage of 26.65. With 18 hours, we get a percentage of 27.72 with a difference of 1.07%, the amount evaporated in the interval between 15 and 18 hours. The variation of evaporation is illustrated in Figure 9.9, which shows the evaporation of two oils, diesel fuel and North Slope Crude, at two temperatures.

9.3.7. A Simplified Means of Estimation This section will present another simple means to calculate the evaporation rate or parameter B in Equation (33).24 This method is not as accurate as the empirical equations such as are presented in Table 9.2. However, the exact evaporation data may not be available. The evaporation parameters (before temperature correction) are the simplest way to proceed. From the work described above, these parameters are given as parameter B above and e0.675 for oils that follow a logarithmic curve and e0.195 for oils that follow a square

228

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

root curve.1 Data on a number of oil evaporation rates, gathered experimentally and basic properties of oil were collected, primarily from a previous paper, and also from literature sources.24-26 While it is known that density and viscosity are not necessarily good indicators of other oil properties, these may be the only data available at the site of a spill, at least until some time passes.21,26 The data show that the general trends of correlation of evaporation parameters with density and viscosity are shown, with significant variance that is only captured by specific oil experiments. The evaporation equation parameters were measured in specific experiments and often are the result of several repeat experiments. The data were correlated with the evaporation parameter in order to derive a simplified model using oil density and viscosity or Saturates, Aromatics, Resins and Asphaltenes (SARA) data.24 The curve-fitting exercise with the logarithmic evaporation data and density and viscosity data resulted in a best-fit simplest equation: Evap ¼ 15:4 14:5 density þ 2:58=viscosity

(39)

where Evap is the evaporation equation parameter and as in Equation (33) is Be0.675 to compensate for the temperature parameter as explained above. Similarly, the data for those oils that evaporate as a square root with time were correlated with density and viscosity. The equation for predicting those oils that evaporate as a square root of time is: Evap ¼ 2:3 1:47 density 0:073 InðviscosityÞ

(40)

where Evap is the evaporation equation parameter or B in Equation (33), but for the square root equivalent. Other data that may be available are SARA data. Statistical analysis shows that only the saturate content correlates, and this does not so well (r2 < 0.6). The addition of the other SARA data only marginally assists in the correlation. However, an equation for the prediction of the evaporation parameter using SARA data is: Evap ¼ 0:048 S þ 0:006 A 0:025 R 0:0003As þ 0:27

(41)

where Evap is the evaporation equation parameter or Be0.675 as in (33) above: S is the saturate content in percent, A is the aromatic content in percent, R is the resin content in percent, and As is the asphaltene content in percent. It is important to understand that such simplified predictions are much less accurate than empirical data such as those shown in Table 9.2.

Chapter | 9

229

Evaporation Modeling

9.4. COMPLEXITIES TO THE DIFFUSION-REGULATED MODEL 9.4.1. Thickness of the Oil Studies show that under diffusion regulation very thick slicks (much more than 5 mm) evaporate slower than other slicks due to the increased path length that volatile components must diffuse in a thicker slick. This can certainly be confused with air-boundary-layer regulation. Figure 9.10 shows the evaporation rate of various thicknesses of oil by the volume to thickness ratio. As can be seen, there is very little difference in this standard presentation. However, one can exaggerate the thickness by a volume factor and this is presented in Figure 9.11. This figure shows that there is a small difference beginning at about 6 mm to about 10 mm. The difference amounts to about 10% slower at 10 mm compared to the rate at about 2 mm. There are few data at very great thicknesses, so this phenomenon requires further investigation. In any case, actual slicks at sea would never reach this thickness. Figure 9.12 shows the concept of slower evaporation with increased path length, that is, increased oil thickness. As noted, there are insufficient data to fully evaluate this at this time, but there is not much motivation as slicks at sea do not reach this thickness.

9.4.2. The Bottle Effect Another confusing phenomenon encountered in trying to understand evaporation is the bottle effect. This effect is illustrated in Figure 9.13. If all the 6.0

Evaporation Rate (%)

5.5 5.0 4.5 4.0 3.5 3.0 2.5

5

10

15

20

Volume to Thickness Ratio FIGURE 9.10 The relationship of volume over thickness (area) to evaporation rate (given as the equation parameter) for one light crude oil. This shows that there is little relationship. Volume largely dictates the evaporation rate; however, for very thick slicks of thickness more than about 10 mm, the evaporation rate slows due to the increased diffusion distance through the liquid.

230 (Volume/Thickness)/Evaporation Rate

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

4.5 4.0 3.5 3.0 2.5 2.0 0

2

4

6

8

10

Thickness mm FIGURE 9.11 A plot of the exaggerated evaporation rate (equation factor, raised by the volume factor) versus oil thickness for one light crude oil. The exaggeration is about an order of magnitude. This shows that evaporation slows somewhat after 6 mm and is slowed more at 10 mm. The effect is only about 10% at 10 mm. This is due to the increased diffusion length through the slick. Typical oil spills are much thinner than about 2 mm, and thus this effect would not be important.

evaporating oil mass is not exposed, such as in a bottle, more oil vapors than can readily diffuse through the air layer at the bottle mouth may yield a partial air-boundary-layer regulation effect. This air-boundary-layer regulatory effect may end when the evaporation of the oil mass lowers past the rate at which the vapors can readily diffuse through the opening. Such effects could occur in reality in situations such as oil under ice, partially exposed to air or when a thick skin forms over parts of the oil, blocking evaporation. During a recent experiment in the Arctic, it was noted that the evaporation rate varied with the amount of exposure to the air.27 This phenomenon was probably caused by a combination of the bottle effect and partially as a result of the increased thickness in the more confined ice situations.

9.4.3. Skinning Several workers have noted that some crude oil and petroleum products form “skins” on their surfaces.28,29 These are largely due to the accumulation of compounds such as resins on the surface, some possibly created by photo oxidation. These can retard the evaporation of the compounds to a great extent. Figure 9.14 shows the results of some evaporation experiments carried out on two oils, Terra Nova crude and Statfjord crude. Simultaneous experiments were carried out on the oils, one of which was stirred, the other not.1 As can be seen in Figure 9.14, the stirred oils evaporated to a greater extent than the unstirred oils. The relevance of this effect at sea may not be as great as wind and waves

Chapter | 9

231

Evaporation Modeling Evaporation limited by diffusion rate through liquid and through surface layer - therefore diffusion regulated Air

Liquid surface

Liquid

Air Liquid surface

In very thick layers, evaporation limited by diffusion rate through liquid but after about 20 to 40 mm, diffusion slows significantly

Liquid

FIGURE 9.12 An illustration of the effect of very great thicknesses of oil. The evaporation rate is slower because of the longer diffusion difference. The difference becomes measurable after about 6 to 10 mm of oil thickness. This is much greater than typical slicks at sea.

may accomplish; the stirring and skin formation may thus be prevented or retarded. It should be noted that during these experiments, one could see the skin formation on the unstirred oil, and this skin developed more toward the end of the 200-hour experiments. Grose used the Mackay and Matsugu equations with some modification and also to account for the skinning factor:30 =ðRKÞ Pi Sk Mi L ¼ C U 0:78 D0:11 (42) o

232

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

More vapor than can easily diffuse through opening - therefore partially air-boundary layer regulated Opening

Air Air

Liquid surface Evaporation limited by diffusion rate through liquid and through surface layer Liquid

FIGURE 9.13 An illustration of the bottle effect. If all the evaporating oil mass is not exposed, more oil vapors than can readily diffuse through the air layer at the bottle mouth may yield a partial air-boundary-layer regulation effect. This regulatory effect may end when the evaporation of the oil mass lowers past the rate at which the vapors can readily diffuse through the opening.

stirred

% Evaporated

40

Statfjord crude

not stirred

stirred

30 not stirred Terra Nova crude

20 Terra Nova Terra Nova - stirred Statfjord Statfjord - stirred

10

0 0

50

100

150

200

Time (Hours) FIGURE 9.14 Results of an experiment to show the effect of “skinning” on oil evaporation. The upper curve in each case is the evaporation of the oil shown with stirring, thus preventing or retarding skin formation. The lower curve is the evaporation without stirring. The effect of skinning for these two oils amounts to several percent differential in evaporation over 200 hours. At sea, wind and waves may mix the oil sufficiently to minimize the skinning effect.

Chapter | 9

Evaporation Modeling

233

where L is the mass of oil evaporated with time (kg/s), C is the environmental transfer constant, U is the wind speed at the surface (m/hr), Do is the diameter of the oiled area (m), K is the oil temperature in Kelvin, Pi is the vapor pressure of the particular component, Sk is the skin factor, and Mi is the molecular-weight equivalent of the particular oil fraction. The skin factor, Sk, ranges from 0.1 to 8 and accounts for the effect of skinning (the formation of a semipermeable surface layer). Yang and Wang suggested a value of Sk ¼ 0.2 after the density of their test oils had increased by 0.78%.13 A value of 1.0 was used in testing the model. In addition, the massloss rate depends on the vapor pressure, Pi, and the molecular weight, MWi, of each fraction. C is a dimensionless environmental transfer constant whose magnitude depends on the units used. The value used for C (0.00024) includes the constant 0.015 after Mackay and Matsugu.8

9.4.4. Rises from the 0-Wind Values Experimentation shows that studies of oil evaporation at absolutely no turbulence or air flow show a slight decrease in evaporation rate from those experiments carried out with slight air movement such as are found in an ordinary room.19 This is due to the slight stirring in the oil mass which increases the diffusion rate somewhat. Tests of this phenomena show that further increases in evaporation rate do not occur with increased air movement or turbulence, thus confirming that this is a phenomenon only at 0-wind or turbulence conditions. These are seen only during capped vessel experiments, and the “jump” in the evaporation rate is seen when the restriction is removed.

9.5. USE OF EVAPORATION EQUATIONS IN SPILL MODELS Evaporation equations are the prime physical change equations used in spill models. This is because evaporation is usually the most significant change that occurs in an oil’s composition. Many models after 1984 have used the Stiver and Mackay approach.9 The equations developed by Mackay and co workers can be implemented in a variety of ways. Often the difference in models is the manner in which the models are applied. Mackay and co workers developed an extensive oil spill model incorporating a number of process equations including evaporation.31 The earlier work of Leinonen and Mackay was used with the modification proposed by Yang and Wang.13,32 The process includes dividing the oil into a number of different fractions and analyzing each fraction for evaporation loss. The mass-transfer function used is the familiar one proposed by Mackay and Matsugu.8 These equations can be solved to obtain V and ci as functions of time. Solutions were developed by assuming a five-component crude oil that spreads on the water surface according to the correlations for the area.

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Payne and co workers developed an oil spill model using the pseudocomponent approach.33-35 Given the boiling point (1 atm) and API gravity of each cut (or pseudocomponent), the vapor pressure of the cut as a function of temperature was calculated. First, the molecular weight and critical temperature of the cut were calculated according to the following correlation: y ¼ C1 þ C2 X1 þ C3 X2 þ C4 X1 X2 þ C5 X12 þ C6 X22

(43)

where y is the vapor pressure of the cut, X 1 is the boiling point (oF) at one atmosphere, X2 is the API gravity, and C16 are constants empirically determined. Similarly, the critical temperature was calculated from the same equation form using the calculated and empirical constant values. Next, the equivalent paraffin carbon number, Nc, was calculated according to: Nc ¼ ðM 2Þ=14

(44)

where M is the molecular weight assigned to the particular cut. The critical volume, Vc, was then calculated according to: Vc ¼ ð1:88 þ 2:44Nc Þ=0:044

(45)

and the critical pressure, Pc, was calculated from: Pc ¼

20:8 Tc þ P0c ðVc 8Þ

(46)

where Tc is the critical temperature and Pc0 correction factor for critical pressure. The factor Pc0 was set to 10 to correct the critical pressure correlation from a strictly paraffinic mixture to a naphtha-aromatic-paraffin mixture. Next the parameter, b, was calculated according to:

where:

b ¼ b0 0:02

(47)

b0 ¼ C1 þ C2 Nc þ C3 Nc2 þ C4 Nc3

(48)

and the values of the constants C 1 to C 4 were calculated and tables of the values are available.19 A final parameter designated as A is then calculated according to: A ¼

Tr b ½log10 ðPrb Þ þ expð20ðTrb bÞ2 Þ Tr b 1

(49)

where A is an intermediary parameter, Trb is the reduced temperature at the normal boiling point, Prb is the reduced pressure at the normal boiling point, and b is an intermediary parameter determined in (48) above.

Chapter | 9

Evaporation Modeling

235

The vapor pressure equation that can be used down to 10 mm Hg can be expressed in terms of A and b as: log10 Pr ¼

Að1 Tr Þ exp½20ðTr bÞ2 Tr

(50)

where Pr is the reduced pressure and Tr is the reduced temperature. A, b, Tc and Pc were determined from the normal boiling point and API gravity of the cut. The temperature at which the vapor pressure is 10 mm Hg was obtained by the root-finding algorithm of Newton-Raphson.34 Below 10 mm Hg, the vapor pressure between two temperatures, Tr1 and Tr2, was calculated according to the Clausius-Clapeyron equation as follows: Z Tr 0:38 2 ð1 T Þ P2 lo r In ¼ dTr (51) P1 RTc Tr1 Tr2 where P1 is the vapor pressure at temperature 1, P2 is the vapor pressure at temperature 2, l0 is the heat of vaporization at 0 K, and Tc is the critical temperature. This was based on the fact that the ratio of the heat of vaporization, l, to (1 e T )0.38 is a constant at any temperature. The latent heat of vaporization was calculated from the slope of the natural log of the vapor pressure equation with respect to the temperature where the vapor pressure is 10 mm Hg. Thus, in the above equation, P2 is the 10 mm Hg vapor pressure at the temperature, Tr, previously determined. Variations of the Payne model were used in older oil spill models. Several models had used the pseudocomponent approach but with the Mackay evaporation method.36 Currently, there are a number of oil spill models using the empirical equations of Fingas.25

9.6. COMPARISON OF MODEL APPROACHES The comparison of air-boundary-layer models with the empirical equations lead to some interesting conclusions on their applicability. Figure 9.15 shows a comparison of the prediction of evaporation of diesel fuel using an air-layerboundary model and an empirical curve. The 0-wind diesel evaporation calculated using an air-layer-boundary model comes closest to the empirical curve. However, prediction is of the wrong curvature. The prediction of diesel evaporation using the wind levels shown results in prediction errors as great as 100% over about 200 hours. Figure 9.16 shows a comparison of the evaporation of Bunker C using two air-layer-boundary models and an empirical curve. The 0-wind evaporation air-boundary-layer prediction comes closest to the empirical curve. As most comparisons show, the evaporation rate up to about 10 hours is similar to the empirical curve. The prediction of Bunker C evaporation

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Percent Evaporated

80

Behaviour of Oil in the Environment and Spill Modeling

Traditional with varying winds

60 Traditional no wind

40 Trad. no wind Trad. 10 m/s Trad. 20 m/s Empirical curve Trad. B. no wind Trad B. 10 m/s Trad. B. 20 m/s

Empirical curve

20

0 0

20

40

60

80

100

120

140

160

Time in Days FIGURE 9.15 A comparison of the evaporation of diesel fuel using an air-layer-boundary model and an empirical curve. The 0-wind diesel evaporation calculated using an air-layer-boundary model comes closest to the empirical curve, but it is of the wrong curvature. The prediction of diesel evaporation using the wind levels shows errors as great as 100% over about 200 hours.

using the wind levels shown results in prediction errors as great as 400% over about 200 hours. These high values of Bunker C evaporation as predicted by air-boundary-layer models with wind conditions are completely impossible, as shown by extensive experimentation and field measurements.19 Figure 9.17 shows a comparison of the evaporation of Prudhoe Bay crude using two airlayer-boundary models with an empirical curve. The 0-wind evaporation prediction using air-boundary-layer methods comes closest to the empirical curve. The evaporation rate calculated by most means up to about 10 hours is similar to the empirical curve. The prediction of Prudhoe Bay evaporation using the wind levels shown results in prediction errors as great as 100% over about 200 hours. These high values of Prudhoe Bay evaporation as predicted by air-boundary-layer models with wind conditions are not realistic. The importance of evaporation modeling can be shown in Figures 9.18 and 9.19. The fate of oil spills is often dictated by evaporation. Accurate modeling of evaporation then becomes of key importance to the useful prediction of oil fate. Thus there are three major errors resulting from the use of air-boundarylayer models. First and most important is that they cannot accurately deal with long-term evaporation; second, the wind factor results in unrealistic values; and finally, they have not been adjusted for the different curvature for diesel-like evaporation. Some modelers have adjusted their models using air-boundary-layer models to avoid very high values at long evaporation times

Chapter | 9

Trad. 0 m/s Trad. 10 m/s Trad. 20 m/s Trad. 40 m/s Trad. 80 m/s Empirical Trad. B 0 m/s Trad. B 10 m/s Trad. B 20 m/s Trad. B 35 m/s

20

Percent Evaporated

237

Evaporation Modeling

15

10 Empirical curve

5

Empirical curve

0 0

20

40

60 80 Time (Hours)

100

120

140

FIGURE 9.16 A comparison of the evaporation of Bunker C using two air-layer-boundary models and an empirical curve. The 0-wind evaporation prediction comes closest to the empirical curve. The prediction of Bunker C evaporation using the wind levels shown results in prediction errors as great as 400% over about 200 hours. These high values of Bunker C evaporation as predicted by air-boundary-layer models with wind conditions are completely impossible. As most comparisons show, the evaporation rate calculated by most means up to about 10 hours is similar to the empirical curve.

by setting a maximum evaporation value. This does avoid very unrealistic high values after a point in time, but does so artificially. Most models of any type will require that one sets a maximum rate to avoid overprediction or values over 100%, for example. This can be best illustrated using a long-term example. A spill in northern Alberta of Pembina oil was sampled 30 years after its spill. Analysis shows that this was weathered to the extent of 58%.37,38 Figure 9.20 shows the comparison of the actual value, the empirical projection, and the air-boundary-layer predicted value. This shows that the air-boundary-predicted value overshoots the estimate by over 60%, despite using only two low wind values of 2 and 7 m/s. Use of higher wind values increases the evaporation to well over 100%.

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Percent Evaporated

40

30

Empirical curve

20

Trad. 0 m/s Trad. 10 m/s Trad. 20 m/s Empirical Curve Trad. B. 0 m/s Trad. B. 10 m/s Trad. B. 20 m/s

Empirical curve

10

0 0

50

100

150

200

250

300

Time (Hours) FIGURE 9.17 A comparison of the evaporation of Prudhoe Bay crude using two air-layerboundary models and an empirical curve. The 0-wind evaporation prediction using air-boundarylayer methods comes closest to the empirical curve. The evaporation rate up to about 10 hours is similar to the empirical curve. The prediction of Prudhoe Bay evaporation using the wind levels shown results in prediction errors as great as 100% over about 200 hours. These high values of Prudhoe Bay evaporation as predicted by air-boundary-layer models with wind conditions are not realistic.

FIGURE 9.18 Highly-evaporated oils can form tar balls that typically strand on beaches. This shows a typical-size tar ball stranded along with other debris.

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Evaporation Modeling

FIGURE 9.19 This shows highly evaporated Bunker C oil, which has just arrived on a beach. In this case, evaporation is a very important factor dictating how an oil spill can be cleaned up.

100 Air-boundary-layer model 7 m/s wind

Percent Evaporated

80

Air-boundary-layer model 2 m/s wind

60 Empirical curve

Analysis value at 30 years

40

Empircal curve Actual analytical Air-Bound., 2 m.s wind Air-Bound. 7 m.s wind

20

0

1 year

0.0

5.0e+4

6 years

1.0e+5

15 years

1.5e+5

2.0e+5

30 years

2.5e+5

Time - Hours FIGURE 9.20 A comparison of the evaporation of Pembina crude using an air-layer-boundary model, an actual analysis after 30 years, and an empirical curve. The evaporation rate up to about 100 hours is similar to the empirical curve. The prediction of long-term evaporation using even small wind levels shown results in prediction errors as great as 60% over about 10 years. These high values of evaporation as predicted by air-boundary-layer models with wind conditions are not realistic.

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9.7. SUMMARY A review of the physics of oil evaporation shows that oil evaporation is not strictly air-boundary-layer regulated. The results of the following experimental series have shown the lack of boundary-layer regulation. 1. A study of the evaporation rate of several oils with increasing wind speed shows that the evaporation rate does not change past the 0-level wind. Water, known to be boundary-layer regulated, does show the predicted increase with wind speed, U (Ux, where x varies from 0.5 to 0.78, depending on the turbulence level). 2. Increasing area does not change the oil evaporation rate. This is directly contrary to the prediction resulting from boundary-layer regulation. 3. The volume or mass of oil evaporating correlates with the evaporation rate. This is a strong indicator of the lack of boundary-layer regulation because with water, volume (rather than area) and rate do not correlate. 4. Evaporation of pure hydrocarbons with and without wind (turbulence) shows that compounds larger than nonane and decane are not boundarylayer-regulated. Most oil and hydrocarbon products consist of compounds larger than these two and thus would not be expected to be boundarylayer-regulated. The fact that oil evaporation is not strictly boundary-layer-regulated implies that a simplistic evaporation equation will suffice to describe the process. The following factors do not require consideration: wind velocity, turbulence level, area, thickness, and scale size. The factors important to evaporation include time and temperature. A comparison of the various models used for oil spill evaporation shows that air-boundary-layer models result in erroneous predictions. There are three issues: air-boundary-layer models cannot accurately deal with long-term evaporation; second, the wind factor results in unrealistic values; and finally, they have not been adjusted for the different curvature for diesel-like evaporation. Modelers have made some effort to adjust air-boundary-layer models to be more realistic for longer-term evaporation, but these may be artificial and result in other errors such as underestimation for long-term prediction. A diffusion-regulated model has been presented along with many empirically developed equations for many oils. The equations are found to be of the form: Percentage evaporated ¼ ½B þ 0:045ðT 15Þ InðtÞ

(52)

where B is the equation parameter at 15 C, T is temperature in degrees Celsius, and t is the time in minutes. It is also noted that with diesel fuel and similar oils, the curve is different and follows a generic curve, such as: pﬃ Percentage evaporated ¼ ½B þ 0:01ðT 15Þ t (53)

Chapter | 9

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Evaporation Modeling

The most accurate predictions are carried out using the empirical equations as noted in Table 9.2. If these are not available, the parameters can be estimated using distillation data, such as: For oils that follow a logarithmic equation: Percentage evaporated ¼ ½:165ð%DÞ þ :045ðT 15ÞlnðtÞ

(54)

For oils that follow a square root equation:

pﬃ Percentage evaporated ¼ ½:0254ð%DÞ þ :01ðT 15Þ t

(55)

180 C,

where D is the percentage distilled at T is the temperature in Celcius, and t is the time in minutes. Equations are also given that allow estimation of evaporation from density, viscosity, or SARA data; however, these are much less accurate than the direct empirically-derived equations.

REFERENCES 1. Fingas MF. The Evaporation of Oil Spills: Development and Implementation of New Prediction Methodology. IOSC 1999;281. 2. Fingas MF. A Literature Review of the Physics and Predictive Modelling of Oil Spill Evaporation. J Haz Mat 1995;157. 3. Jones FE. Evaporation of Water. Chelsea, Michigan: Lewis Publishers; 1992. 4. Brutsaert W. Evaporation into the Atmosphere. Dordrecht, Holland: Reidel Publishing Company; 1982. 5. Fingas MF. Studies on the Evaporation of Crude Oil and Petroleum Products: I. The Relationship between Evaporation Rate and Time. J Haz Mat 1997;227. 6. Monteith JL, Unsworth MH. Principles of Environmental Physics. London: Hodder and Stoughton; 1990. 7. Sutton OG. Wind Structure and Evaporation in a Turbulent Atmosphere. P Royal Society of London 1934;701. 8. Mackay D, Matsugu RS. Evaporation Rates of Liquid Hydrocarbon Spills on Land and Water. Can J Chem Eng 1973;434. 9. Stiver W, Mackay D. Evaporation Rate of Spills of Hydrocarbons and Petroleum Mixtures. Env Sci Tech 1984;834. 10. Blokker PC. Spreading and Evaporation of Petroleum Products on Water. Proceedings of the Fourth International Harbour Conference, 1964;911. 11. Goodwin SR, Mackay D, Shiu WY. Characterization of the Evaporation Rates of Complex Hydrocarbon Mixtures under Environmental Conditions. Can J Chem Eng 1976;290. 12. Butler JN. Transfer of Petroleum Residues from Sea to Air: Evaporative Weathering. In: Windom HL, Duce RA, editors. Marine Pollutant Transfer, 201. Toronto: Lexington Books; 1976. 13. Yang WC, Wang H. Modelling of Oil Evaporation in Aqueous Environments. Water Res 1977;879. 14. Brighton PWM. Further Verification of a Theory for Mass and Heat Transfer from Evaporating Pools. J Haz Mat 1990;215. 15. Brighton PWM. Evaporation from a Plane Liquid Surface into a Turbulent Boundary Layer. J Fluid Mech 1995;323. 16. Stiver W. Weathering Properties of Crude Oils wWhen Spilled on Water, Master of Applied Science Thesis, Department of Chemical Engineering and Applied Chemistry. Toronto: University of Toronto; 1984:4,110,132.

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17. Brown HM, Nicholson P. The Physical-Chemical Properties of Bitumen. AMOP 1991;107. 18. Bobra M. A Study of the Evaporation of Petroleum Oils, Manuscript Report Number EE-135. Ottawa, ON: Environment Canada, 1992. 19. Fingas MF. Studies on the Evaporation of Crude Oil and Petroleum Products: II. Boundary Layer Regulation. J Haz Mat 1998;41. 20. Ullmann Encyclopedia. Hamburg: Ullmann Publishing; 1989-2005. 21. Jokuty PS, Whiticar S, Wang Z, Fingas MF, Fieldhouse B, et al. Properties of Crude Oils and Oil Products. Environment Canada Manuscript Report EE-165, Ottawa, ON 1999. 22. Fingas MF. The Evaporation of Oil Spills: Prediction of Equations Using Distillation Data. Spill Sci Tech Bull 1996;191. 23. Fingas MF. The Evaporation of Oil Spills: Prediction of Equations Using Distillation Data. AMOP 1997;1. 24. Fingas MF. Estimation of Oil Spill Behaviour Parameters from Readily-Available Oil Properties. AMOP 2007;1. 25. Fingas MF. Modeling Evaporation Using Models That Are Not Boundary-Layer Regulated. J Haz Mat 2004;27. 26. Oil Catalogue, http://www.etc-cte.ec.gc.ca/databases/spills/oil_prop_e.html, accesssed April, 15 2010. 27. Brandvik PJ, Faksness L-G. Weathering Processes in Arctic Oil Spills: Meso-Scale Experiments with Different Ice Conditions. Cold Reg Sci Technol 2009;160. 28. Bobra M, Tennyson EJ. Photooxidation of Petroleum. AMOP 1989;129. 29. Garrett RM, Pickering IJ, Haith CE, Prince RC. Photooxidation of Crude Oils. Environ Sci Technol 1998;3719. 30. Grose PL. A Preliminary Model to Predict the Thickness Distribution of Spilled Oil, Proceedings of a Workshop on The Physical Behaviour of Oil in The Marine Environment, Princeton University, 1979. 31. Mackay D, Buist I, Mascarenhas R, Paterson S. Oil Spill Processes and Models, EE-8. Ottawa, ON: Environment Canada, 1980. 32. Leinonen PJ, Mackay D. A Mathematical Model of Evaporation and Dissolution from Oil Spills on Ice, Land, Water, and Under Ice, Proceedings Tenth Canadian Symposium 1975: Water Pollution Research Canada, 1975;132. 33. Payne JR, McNabb Jr GD, Hachmeister LE, Kirstein BE, Clayton Jr JR, et al. Development of a Predictive Model for the Weathering of Oil in the Presence of Sea Ice, Outer Continental Shelf Environmental Assessment Program, vol. 59, NOAA, 1988. 34. Payne JR, Kirstein BE, McNabb Jr GD, Lambach JL, Redding R, et al. Multivariate Analysis of Petroleum Weathering in the Marine EnvironmentdSubarctic, U.S. Department of Commerce, NOAA, OCSEAP, vol. 21, 1984. 35. Payne JR, McNabb Jr GD. Weathering of Petroleum in the Marine Environment, Marine Technology Society Journal, Washington, DC, 1984;24:18. 36. Buist I, Belore R, Guarino A, Hackenberg D, Dickins D, Wang Z. Empirical Weathering Properties of Oil in Ice and Snow. AMOP 2009;56. 37. Wang Z, Fingas M, Yang C, Hollebone B, Peng X. Biomarker Fingerprinting: Applications and Limitations for Source Identification and Correlation of Oils and Petroleum Products. AMOP 2004;103. 38. McIntyre CP, Harvey PM, Ferguson S, Wressnig AM, Snape I, George SC. Determining the Extent of Weathering of Spilled Fuel in Contaminated Soil Using the Diastereomers of Pristane and Phytane. Org Geochem 2007;2131.

Chapter 10

Models for Water-in-Oil Emulsion Formation Merv Fingas

Chapter Outline 10.1. Introduction 10.2. Early Modeling of Emulsification 10.3. First Two Model Developments 10.4. New Model Development

243 249 251

10.5. Development of an Emulsion Kinetics Estimator 10.6. Discussion 10.7. Conclusions

260

260 269

253

10.1. INTRODUCTION Water-in-oil emulsions sometimes form after oil or petroleum products are spilled. These emulsions, often called chocolate mousse or mousse by oil spill workers, make the cleanup of oil spills very difficult.1 When water-in-oil emulsions form, the physical properties and characteristics of oil spills change dramatically. For example, stable emulsions contain from 60 to 90% water, thus expanding the spilled material from 2 to 5 times the original volume. Most significantly, the viscosity of the oil typically changes from a few hundred mPa.s to about 100,000 mPa.s, an increase of 500 or more. A liquid product is changed into a heavy, semisolid material. These emulsions are difficult to recover with conventional spill recovery equipment. Recently, Fingas and Fieldhouse published a paper on emulsion formation as it relates to spilled oil.2 They found that four clearly defined water-in-oil types are formed by oil and petroleum products when mixed with water. This was shown by water resolution over time, by a number of rheological measurements, and by the water-in-oil product’s visual appearance, both on the day of formation and one week later. Some emulsions were observed for over a year, with the same results. The types are named stable water-in-oil Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10010-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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emulsions, mesostable water-in-oil emulsions, entrained, and unstable waterin-oil emulsions. The differences among the four types are quite large and are based on at least two water-content measurements and five rheological measurements. More than 300 oils or petroleum products were studied. Fingas and Fieldhouse carried out tests of several indices of stability, a single value that could provide good discrimination between water-in-oil types even on day one.2 It was found that all of these indices could be used to some extent to differentiate the four water-in-oil types. One index of stability was the ratio of the complex modulus of the product on the first day divided by the starting oil viscosity. This index was named stability A. Stability B was the elastic modulus on the first day divided by the starting oil viscosity. Stability C was a combination of these two indices, the log of the cross product of stability A and B. Another index, the viscosity increase, the ratio of the oil viscosity after mixing divided by the starting oil viscosity, was also evaluated. Another test, first day water times stability A, was a simple composite of the first day water content times stability A. The last two parameters evaluated were the tan delta on the day of formation and the change in tan delta over one week. The index called stability C proved to be the index that best differentiated all four water-in-oil types. Scientists reported that asphaltenes were a major factor in water-in-oil emulsions more than 40 years ago.3 The specific roles of asphaltenes in emulsions have not been defined until recently. The basics of water-in-oil emulsification are now understood to a much better degree.2,4-6 The basic principle is that water-inoil emulsions are stabilized by the formation of high-strength viscoelastic asphaltene films around water droplets in oil. Resins also form emulsions, but they do not form stable emulsions, and they actually aid in asphaltene emulsion stability by acting as asphaltene solvents and by providing temporary stability during the time of the slow asphaltene migration. In all, a wide spectrum of researchers have found out that oil composition is a key factor to water-in-oil emulsion formation and that several key factors include the asphaltene, resin, and saturate contents as well as the density and viscosity of the starting oil. McLean and Kilpatrick studied asphaltene aggregation in model emulsions made from heptane and toluene.7 The asphaltenes were extracted using heptane and the resins using open-column silica columns. Although some emulsions could be generated using resins, they were much less stable than those generated by asphaltenes. It was also found that the concentration of asphaltenes and the availability of solvating resins were important. McLean and Kilpatrick put forward the thesis that asphaltenes were most effective in stabilizing emulsions when they are near the point of incipient precipitation.8 It was noted that there are specific resineasphaltene interactions because differing combinations yielded different results in the model emulsions. The most effective emulsion resin and asphaltene stabilizers were the most polar and the most condensed. McLean and Kilpatrick concluded that the most significant factor in emulsion formation is the solubility state of asphaltenes.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

245

Subsequently, several workers reviewed emulsions and concluded that the asphaltene content is the single most important factor in the formation of emulsions.9-11 Even in the absence of any other possibly-synergistic compounds such as resins, asphaltenes were found to be capable of forming rigid, cross-linked, elastic films that are the primary agents in stabilizing waterin-crude oil emulsions. The exact conformations by which asphaltenes organize at oilewater interfaces and the corresponding intermolecular interactions have not been elucidated. McLean and colleagues suggest that the intermolecular interactions must be either p-bonds between fused aromatic sheets; H-bonds mediated by carboxyl, pyrrolic, and sulfoxide functional groups; or electron donoreacceptor interactions mediated by porphyrin rings, heavy metals, or heteroatomic functional groups.9 Workers studying only crude oil emulsions concluded that water-in-oil emulsions are exclusively stabilized by asphaltenes.12 Even though the emulsions contain inorganic solids, waxes, and other organic solids, the main stabilization comes from asphaltenes. Other workers have noted that solid particles, such as clays, when present, can stabilize or contribute to the stabilization of emulsions.13 This is true of emulsions formed by clay-containing bitumens. These clay-stabilized emulsions may have differences from the crude oil and petroleum product emulsions noted in this subchapter. Asphaltenes are defined by their precipitation from oil in pentane, hexane, or heptane. The specific structure of asphaltenes is unknown; however, the molecular weight averages about 750, and there is a planar aromatic structure surrounded by alkane groups, some with heteroatoms, S, N, and O.14 Studies of the time dependency of film strength by viscosity measurements showed that the complex modulus increased about twofold between 2 and 4 hours. This indicates an increased film strength, probably due to asphaltene aggregation and cross-linking. The mechanism by which emulsions form begins when asphaltenes migrate to the oilewater interface, a process that is regulated by the diffusion of the soluble asphaltenes. At the interface, the asphaltenes selfassemble to produce an elastic, rigid, stable film.5,15,16 The absorption of asphaltenes at the watereoil interface proceeds for a long time and may still proceed after a year.17 This implies that the absorption at the interface lowers the net energy of the system and thus is favored thermodynamically. The bulk concentration of asphaltenes is important and drives the amount that is absorbed at the interface. Asphaltenes have their greatest tendency to absorb and make the strongest interfacial film at their limit of solubility. Several workers noted that there were differences in the stability of emulsions, depending on the fractions of asphaltenes taken and also by the amount of asphaltene aggregation present.18,19 Asphaltene aggregation may be an important topic in emulsion stability. Asphaltenes may self-associate in a manner that is oligimerization rather than simple stacking. Several researchers studied the role of resins in water-in-oil formation.2,12 They noted that the main role appears to be solvation of the asphaltenes in the

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oil solution. They conducted experiments showing that the addition of resins at ratios of 2:1 (resins:asphaltenes) could increase the stability of the water-in-oil emulsions by as much as twice. Others have noted that resins and asphaltenes are somehow tied together in emulsion stability. Some researchers noted that with increasing asphaltene:resin ratio, the emulsions in wellheads were more stable.20 The interfacial properties of asphaltenes in several Norwegian offshore crude oils were studied.21 Asphaltenes were shown to be the agent responsible for stabilizing the crude oils tested; however, the resins were also noted as being important. Some effect from naphthenic acids was also observed.22 Sjo¨blom and coworkers noted that many of the stability differences in emulsions can be explained by the interactions between asphaltenes and resins.23 They stated that asphaltenes are believed to be suspended as colloids in the oil with stabilization by resins. Each particle is believed to consist of one or more sheets of asphaltene monomers and absorbed resins to stabilize the suspension. Under certain conditions, the resins can desorb from the asphaltenes, leading to increased asphaltene aggregation and precipitation of the larger asphaltene aggregates. Several workers then noted that there is significant interplay between asphaltenes and resins and that resins solvate the asphaltenes.24,25 The availability of methodologies to study emulsions is very important. In the past 15 years, dielectric methods and rheological methods and many other methods have been exploited to study formation mechanisms and stability of emulsions.5,15 Standard chemical techniques, including nuclear magnetic resonance (NMR), chemical analysis techniques, near-infrared spectroscopy (NIR), microscopy, interfacial pressure, and interfacial tension, are also being applied to emulsions. These techniques have largely confirmed findings noted in the dielectric and rheological mechanisms. The use of high-pressure NIR has been used by one group to study asphaltene aggregation of live crude oils.23,26,27 Further NIR information on the amount of asphaltenes and resins was tied to the emulsion stability.27 Many of the measurements can now be intercorrelated. Most researchers studied the stability of emulsions by measuring the amount of water resolved with time.10,12,28-32 This certainly is the absolute means. Some researchers also subjected the emulsions to centrifugation to assess stability. Dielectric spectroscopy has been used to study emulsions. The electrical permittivity of the emulsion can be used to characterize an emulsion and assign a stability.5,22,25,33,34 The Sjo¨blom group has measured the dielectric spectra using the time domain spectroscopy (TDS) technique. A sample is placed at the end of a coaxial line to measure total reflection. Reflected pulses are observed in time windows of 20 ns and Fourier transformed in the frequency range from 50 MHz to 2 GHz. Many studies on the rheology of emulsions have been performed.35-37 Emulsions stabilized by surfactant films (such as resins and asphaltenes)

Chapter | 10 Models for Water-in-Oil Emulsion Formation

247

behave like hard-sphere dispersions and display viscoelastic behavior. Relaxation time can be determined for the system, which increases with the volume fraction of the discontinuous phase. It has been noted that the emulsion stability is highly dependent on the rheological properties of the water/oil interface and that a high elasticity will increase the level of stability.6 Several workers in the field have used the interface forces on single droplets to study emulsions. One group used an oscillating pendant drop apparatus and correlated this to asphaltene solubility as measured by NIR.38 Yarranton et al. report on the use of interfacial rheology to study emulsion coalesence.39 Asphaltenes from Athabasca Bitumen were mixed with a solvent and injected into an optical cuvette and the droplet was monitored by camera. Interfacial tension was measured from static measurements on the droplet. Elasticity was measured by manipulating the droplet so that sinusoidal oscillations occurred and the elasticity was measured using Fourier analysis. The findings were that the interfacial modulus was a function of asphaltene content and reached a maximum at an asphaltene concentration of about 1 kg/m3. The modulus increased as the interface aged. The data were found to be consistent with the gradual formation of a cross-linked asphaltene network on the interface. Moran et al. studied emulsions using a micropipette technique in which individual emulsion drops are elongated into a cantilever for force measurements.40 The stress-strain measurements are converted to interfacial rheological behavior. Khristov et al. studied emulsions as a film with toluene dilution and the thin liquid film-pressure balance technique.41 Many researchers studied emulsification by using model oils or modified crude oils. Other researchers studied emulsions as thin layers or by droplets.8,42 Hemmingston et al. studied the water-in-oil emulsions formed from 27 crude oils of different origins.43 They found an increase in stability as viscosity increases. Viscosity also correlated with the SARA (Saturates, Aromatics Resins, and Asphaltenes) data taken on the crude oil. Sjo¨blom et al. studied 16 crudes from the North Sea and 5 from North Africa.34 Stability was measured using the results from an electric field cell. A voltage was applied to the cell until coalescence was observed using a microscope. The value of the voltage at the critical point was taken as a measure of stability. The findings of Fingas and Fieldhouse, studying over 400 crudes and petroleum products, point to four groups of emulsions formed by crude oils and petroleum products: stable, mesostable, entrained, and unstable.2 Stable emulsions are reddish-brown solid-like materials with an average water content of about 80% on the day of formation and about the same one week later. Stable emulsions remain stable for at least 4 weeks under laboratory conditions. All of the stable emulsions studied remained so for at least one year. The viscosity increase over the day of formation averages a multiple of 400 and one week later averages 850. The average properties of the starting oil required to form a stable emulsion are: density 0.9 g/mL; viscosity 300 mPa.s; resin content 9%; asphaltene content 5%; and asphaltene-to-resin ratio 0.6.

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Mesostable water-in-oil emulsions are reddish-brown viscous liquids with an average water content of 64% on the first day of formation and less than 30% one week later. Mesostable emulsions generally break down fairly completely within one week. The viscosity increase over the day of formation averages a factor of 7 and one week later averages 5. The average properties of the starting oil required to form a mesostable emulsion are: density 0.9 g/mL; viscosity 1300 mPa.s; resin content 16%; asphaltene content 8%; and asphaltene-to-resin ratio 0.5. The greatest difference between the starting oils for stable and mesostable emulsions are the ratio of viscosity increase (stable 400, first day and 850 in one week; mesostable 7, first day and 5 in one week) and resin content (stable e9%; mesostable e16%). Entrained water-in-oil types are black viscous liquids with an average water content of 45% on the first day of formation and less than 28% one week later.2 The viscosity increase over the day of formation averages a multiple of two and one week later still averages two. The average properties of the starting oil required to form entrained water are: density 0.97 g/mL; viscosity 60,000 mPa.s; resin content 18%; asphaltene content 12%; and asphaltene-to-resin ratio 0.75. The greatest differences between the starting oils for entrained water-in-oil compared to stable and mesostable emulsions are the viscosity of the starting oil (entrained starting oil averages 60,000 mPa.s compared to 200 mPa.s for stable emulsions and 1300 mPa.s for mesostable emulsions) and the ratio of viscosity increase (entrained ¼ 2, first day and 2 in one week; stable 400, first day and 850 in one week; mesostable 7, first day and 5 in one week). Entrained water-in-oil types appear to be applicable to viscous oils and petroleum products, but not extremely viscous products. Unstable water-in-oil emulsions are characterized by the fact that the oil does not hold significant amounts of water. There is a much broader range of properties of the starting oil than for the other three water-in-oil states. For example, viscosities are very low or very high. Included in this group are light fuels such as diesel fuel and very heavy, viscous oil products. The data suggest that the water-in-oil types are stabilized by both asphaltenes and resins, but that, for greater stability, resin content should exceed the asphaltene content slightly.2 Excess resin content (A/R > about 0.6) apparently destabilizes the emulsion. This does not consider the question of different types of asphaltenes or resins. A high asphaltene content (typically > 10%) increases the viscosity of the oil such that a stable emulsion will not form. Viscous oils will only uptake water as entrained water and will slowly lose much of this water over a period of about one week. Viscous oils (typically >1000 mPa.s) will not form stable or mesostable emulsions. Oils or low viscosity or without significant amounts of asphaltenes and resins will not form any water-in-oil type and will retain less than about 6% water. Oils of very high viscosity (typically > 10,000 mPa.s) will also not form any of these water-in-oil types and thus are classified as unstable. This is probably due to the inability of water droplets to penetrate the oil mass.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

249

Several new models for the prediction of water-in-oil emulsions were recently developed.44-48 These models were the first available to calculate the formation of emulsions using a continuous function and employing the physical and chemical properties of oil. Since this initial model was developed, the emulsification properties of more oils were measured, and the properties of some of the oils in the existing set oils have been remeasured. Some of these data were found to be deficient and were eliminated. This enables the models to be recalculated with sound data on over 316 oils out of over 400 studied. The basis of these models is the result of the knowledge demonstrated abovednamely, that models are stabilized by asphaltenes, with the participation of resins. Other components of oil, especially aromatics, may actually interfere with this process. Findings of this group and other groups show that the entire SARA (Saturates, Aromatic, Resins, and Asphaltene) component affects the formation of emulsions as the prime stabilizers; asphaltenes, and secondarily resins, are available for emulsion formation only when the concentration of the saturates and aromatics are at a certain level and when the density and viscosity are correct.2 The very early emulsion formation modeling equations at that time did not use specific knowledge of emulsion formation processes.44 In the past, the rate of emulsion formation was assumed to be first-order with time. This was then approximated with a logarithmic (or exponential) curve. Although not consistent with the knowledge of how emulsions formed, this assumption has been used extensively in oil spill models. The processes outlined in the introduction above were not discovered until many years after the simple assumptions were proposed. Furthermore, the presence of different water-in-oil states dictates that one simple equation is not adequate to predict emulsion formation. The stability of an emulsion, as represented by stability C, was correlated with starting oil properties to start model development. The different waterin-oil types could be differentiated by correlating individually with starting oil viscosity, density, saturate content, asphaltene content, resin content, or asphaltene/resin ratio. Combinations of these oil properties yielded even greater differentiation between water-in-oil types. Examples of good separation of water-in-oil types were achieved by plotting asphaltene content times asphaltene/resin ratio, along with starting oil viscosity and stability. Other correlations with similar input also yielded differentiation between water-in-oil types.

10.2. EARLY MODELING OF EMULSIFICATION The emulsification processes described above were not apparent until about 15 years ago and have since been translated into modeling equations. Furthermore, the presence of different water-in-oil states dictates that one simple equation is not adequate to predict formation. Information on the kinetics of formation at sea and other modeling data was less abundant in the past. It is now known that emulsion formation is a result of surfactant-like behavior of the polar

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asphaltene and resin compounds. While these are similar compounds that both behave like surfactants when they are not in solution, asphaltenes form much more stable emulsions. Emulsions begin to form when the required chemical conditions are met and when there is sufficient sea energy. Furthermore, as pointed out above, three different water-in-oil types are producedddepending on the oil type and its state of weathering. In the past, the rate of emulsion formation was assumed to be first-order with time. This can be approximated with a logarithmic (or exponential) curve. The physical assumption is that all oils uptake water on a first-order basis. Although not consistent with the knowledge of how emulsions formed, this assumption has been used extensively in oil spill models. As will be shown later, this does not yield correct results. Most models that incorporate the phenomenon use the estimation technique of Mackay and co workers or a variation of this technique.49-51 Mackay suggested the following equation to model water uptake: DW ¼ Ka ðU þ 1Þ2 ð1 Kb WÞDt;

(1)

where DW is the water uptake rate, W is the fractional water content, Ka is an empirical constant, U is the wind speed, Kb is a constant with the value of approximately 1.33, and t is time. Because Equation (1) predicts that most oils will form emulsions rapidly given a high wind speed, most users have adjusted the equation by changing constants or the form slightly. Mackay and Zagorski proposed two relationships to predict the formation of emulsions on the sea. They proposed that the stability could be predicted as follows:51 S ¼ Xa ga exp½Kao ð1 xa xw Þ2 þ Kaw x2w exp½0:04ðT293Þ ;

(2)

where S is the stability index in relative units, high numbers indicate stability, xa is the fraction of asphaltenes, ga is the activity of asphaltenes, Kao is a constant which here is 3.3, xw is the fraction of waxes, Kaw is a constant which is 200 at 293 K, and T is the temperature in kelvin. Water uptake was given as: DWT ¼ DWL þ DWS ¼ DT½kf k1 W1 ;

(3)

where DWT is the total change in water content, DWL is the change in water content for large droplets, DWs is the change in water content for small droplets, DT is time, kf is the rate constant for formation, typically 1 h1, kl is the rate constant for large droplet formation and is about 3 h1, and Wl is the fraction of large droplets, which is typically 3 to 4. Kirstein and Redding used a variation of the Mackay equation to predict emulsification:52 ð1 k2 WÞ exp

2:5W ¼ exp ðk5 k3 tÞ; 1 k1 W

(4)

Chapter | 10 Models for Water-in-Oil Emulsion Formation

251

where k2 is a coalescing constant that is the inverse of the maximum weight fraction water in the mixture, W is the weight fraction water in the mixture, k1 is the Mooney constant that is 0.62 to 0.65, k5 is the increase in mousse formation by weathering, k3 is the lumped water incorporation rate constant and is a function of wind speed in knots, and t is the time in days. The change in viscosity due to emulsion formation was given by: m ¼ m0 exp

2:5W ; 1 k1 W

(5)

where m is the resulting viscosity, m0 is the starting oil viscosity, and the remainder are identical to the above. Reed used the Mackay equations in a series of models.53 The constants were adjusted to correspond to field observations: dFwc Fwc ¼ 2 105 ðW þ 1Þ2 ð1 Þ; dt C3

(6)

where dFwc/dt is the rate of water incorporation, W is the wind speed in m/s, Fwc is the fraction of water in oil, and C3 is the rate constant equal to 0.7 for crude oils and heavy fuel oils. The viscosity of the emulsion was predicted using the following variant of the Mooney equation: m 2:5Fwc ¼ exp ; 1 0:65Fwc m0

(7)

where m is the viscosity of the mixture, m0 is the oil starting viscosity, and Fwc is the fraction of water in oil. The effect of evaporation on viscosity was modeled as: m ¼ m0 expðC4 Fevap Þ;

(8)

where m is the viscosity of the mixture, m0 is the viscosity of the starting oil, C4 is a constant that is 1 for light fuels and 10 for heavy fuels, and Fevap is the fraction evaporated from the slick. All of the above work has a basis in the Mackay equations, which were developed before extensive work on emulsion physics took place. It is now known that both the formation and the characteristics of emulsions could be predicted with orders of magnitude more accurately using empirical data as described below.

10.3. FIRST TWO MODEL DEVELOPMENTS The approaches to model development were implemented and are detailed in the literature.44,45 The approaches used empirical data. One approach was to curve fit the physical and content data to the “stability” index, which was at that time the complex modulus of the water-in-oil product divided by the starting oil

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viscosity. Then this stability factor was used in turn to predict a class (stable, mesostable, entrained, or unstable). Another approach was to predict the class directly from the data which approach was later used in an “improved” model. It is important to note that the class was assigned an arbitrary numerical value, which was later adjusted to optimize the fit. This contrasts very much with the new approach described in this chapter, which uses a newly developed stability index. The empirical data were used to develop mathematical correlation.46 The value for each parameter was correlated in a series of models using DataFit (Oakdale Engineering), which calculates linear models. A two-step process is necessary as DataFit is not able to calculate the specific mathematical function with more than two variables, due to the large number of possibilities. Thus, the functions (e.g., linear, square, log) were calculated using a two-way regression (TableCurve), and these functions were in turn used in developing a predictor model for emulsification. The model that predicts class directly will be summarized here. The steps to produce the model are summarized in an earlier paper.45,46 First, the parameters available were correlated one at a time. Regression coefficients were optimized by adjusting the class criteria from a starting value of 1 to 4 and recalculating these class values several times until they remained relatively stationary. The resulting criteria were: 0.608 is unstable; 0.686 is entrained; 0.657 is mesostable; and 0.674 is stable. The input parameters were density, viscosity, saturates, resins, asphaltenes, a/rdthe asphalteneeresin ratio, and the aromatic content. Several of these parameters can have a zero value, which causes calculation problems. If this is the case, the zero is adjusted either to delete these values or to adjust it to the typical high value for the parameter. A second transformation is performed to adjust the data to a singular increasing or decreasing function. Most parameters have an optimal value with respect to class; that is, the values have a peak function with respect to stability or class. After this correction is made to the values, the regression coefficient increases. Arithmetic was used to convert values in front of the peak to values behind the peak, thus yielding a singular declining or increasing function. The optimal value of this manipulation is found by trial and error, beginning with the estimated peak from the first correlation. The arithmetic to perform this manipulation is as follows: if the initial value is less than the peak value, then the adjusted value is the peak value less the initial value; and if the initial value is more than the peak value, the adjusted value is the initial value less the peak value. The values were adjusted by trial and error until a maximum regression coefficient for the whole model was achieved. The values of the second round of calculations were then correlated using the multiparameter regression package, DataFit. The best model was one that included only four input parameters: density, viscosity, resins, and asphaltenes. It was found that the regression coefficients of class with aromatic content, asphaltene/resin ratio, and waxes were too low to include in the model.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

253

Saturates were eliminated in this new round as it was found that this parameter did not contribute to the overall model. There are some problems with the fundamental process of categorizing water-in-oil states at the onset. Some crude oils are enhanced by the addition of emulsion preventors (also called asphaltene suspenders) directly at the wellhead. This is because they are very emulsion prone. Thus, some emulsion-prone oils may not form emulsions during the laboratory or field tests because of the addition of these emulsion-preventing materials. Although attempts are made to receive oils that do not contain these emulsion-preventing materials, it is impossible to know this fact in every case. Several models were achieved. The best value obtained is 0.996 for the revised model; this was a significant improvement over the old value of 0.51. The fit of the class of water-in-oil type was over 70% correct. Oils that form the entrained state generally have densities over 0.96 and viscosities exceeding 10,000 mPa.s. This can be used to discriminate the entrained states from the other states, which may overlap.

10.4. NEW MODEL DEVELOPMENT The older modeling method as noted in Section 10.3, used an assigned number for each water-in-oil type, and then this was regressed with the various oil starting properties. Further, the functional relationship (square, log, exponential, etc.) of the starting oil properties was determined beforehand and then correlated with a multiparameter linear method to yield a predictor model. Since a stability index has been established, this can be used as the target of the correlation.2 This new modeling has a different approach than the old one. Furthermore, this new approach used a multiregression program directly, using various multifunctional transformations of the input oil property data. This will allow the software of the regression model to assign portions of the functions necessary to achieve the highest correlation factor. Other differences are also that the transformations are automated using a peak function fit, yielding slightly different transformation values than the old values; use of a newer data set; and incorporation of several functional input parameters, dependent on the statistics of the model, into the final equation. A transformation is needed to adjust the data to a singular increasing or decreasing function. Regression methods will not respond correctly to a function that varies both directly and inversely with the target parameter. Most parameters have an optimal value with respect to class; that is, the values have a peak function with respect to stability or class. This is illustrated in Figure 10.1 and Figure 10.2. The resin content without any adjustment is plotted against the stability in Figure 10.1. The stability as shown in these figures is Stability C, as described above. As can be seen in this figure, the values of stability peak at about 5% resins. After this correction is made to the values, the regression coefficient increases. The modified distribution is shown

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30

20

Index

10

0

–10

–20

–30 0

10

20

30

Resins FIGURE 10.1 Graph of resin content (uncorrected) versus stability parameter C. The curve fit is the calculated peak. This is typical of the scatter associated with multiple parameter fits, where each parameter only accounts for a small amount of the variance.

in Figure 10.2. The arithmetic converts values in front of the peak to values behind the peak, thus yielding a singular declining function. The optimal value of this manipulation is found by using a peak function as shown by the line in Figure 10.1. This peak function fit is available from TableCurve, regression software. In the past this was achieved by trial and error, beginning with the estimated peak from the first correlation. The accuracy of the new method is obviously much greater. It is important to note that the scatter observed in Figures 10.1 and 10.2 are typical of scatter observed for a single parameter when a large number of parameters are operative. If one plotted the total number of parameters simultaneously (e.g., in multidimensions), then a singular function would appear with much less scatter. The arithmetic to perform the transformation is as follows: if the initial value is less than the peak value, then the adjusted value is the peak value less the initial value; and if the initial value is more than the peak value, then the adjusted value is the initial value less the peak value. The values found for the transformations are listed in Table 10.1. It should be noted that the exponential of density was used along with the natural log of the viscosity. Previous modeling work had shown that these mathematical changes are necessary to achieve higher correlations.

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Chapter | 10 Models for Water-in-Oil Emulsion Formation

30

20

Index

10

0

–10

–20

–30 0

5

10

15

20

25

Resins FIGURE 10.2 Graph of corrected resin content versus stability parameter C. The curve fit is the best fit. This amount of scatter is typical of multiple parameter fits, where each parameter accounts for only a small amount of the variance.

Having the transformed values, the new model proceeds directly to fit a multiple linear equation to the data. The choice of functions was achieved by correlating the stability function directly with the data and taking the best of the functions (e.g., square, log, etc.) further into the regression process. The functionalities of square, logarithmic or exponential curves are

TABLE 10.1 Values Used to Correct Oil Property Input Parameters Parameter

Form

Correction Value

Density

Exponential

2.5

Viscosity

Natural Logarithm

5.8

Saturates

Standard %

45

Resins

Standard %

10

Asphaltenes

Standard %

4

Asphaltene/Resin Ratio

Standard

0.6

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achieved by correlating the straight value of the input properties plus their expanded values, taken here as the cube of the starting parameter as well as the square of the exponential of the starting value; and their companded values, the natural log (ln), and the logarithm (base 10) of the parameter divided by the square of the value. Thus each parameter is correlated with the stability C index in five sets of mathematical statements. This is similar to the standard Gaussian expansion regression technique.54 In this method the regression is expanded to functionalities above and below linear until the entire entity is optimized. For example, a linear function would be included, then a square, and then a square root and so on until tests of the complete regression show that there are no more gains in increased expansions. Using this technique, six input parametersdexponential of density, ln of viscosity, saturate content, resin content, asphaltene content, and the A/R ratiod were found to be optimal. Thus with four transformations and the original values of these input parameters, there are six times five, or 30, input combinations. Using Datafit, a multiple regression software, a maximum of 20 of these could be taken at a time to test the goodness-of-fit. Values that yield Prob(t) factors of greater than 0.9 were dropped until all remaining factors could be calculated at once. The Prob(t) is the probability that input can be dropped without affecting the regression or goodness-of-fit. Over 20 regressions were carried out until the resulting model was optimal. The r2, the regression coefficient, was 0.75, which is quite high considering the many potential sources of error, and so on. The statistics on the new model are shown in Table 10.2, along with the parameters to create the model. Table 10.2 shows that the 14 remaining parameters all contribute to the accuracy of the final result and that none of them can be cut without affecting the outcome of the model. The procedures for using the new model are given below. The first step is to transform the input data so that it forms a continuous declining or increasing function. Density: take the exponential of the density. If the exponential of density is less than 2.5, then the density parameter is 2.5 less the density and if it is greater than 2.5, it becomes the density less 2.5. The value used in the equation is this transformed value. (9) Viscosity: take the natural logarithm (ln) of the viscosity. If the natural log of the viscosity is less than 5.8, then the viscosity parameter is 5.8 less the viscosity natural log, and if it is greater than 5.8, it becomes the natural log of viscosity less 8.7. The value used in the equation is this transformed value. (10) Saturate content: if the saturate content is less than 45, then the saturate content parameter is 45 less the saturate content, and if it is greater than 45, it becomes the saturate content less 45. The value used in the equation is this transformed value. (11)

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Chapter | 10 Models for Water-in-Oil Emulsion Formation

TABLE 10.2 Regression Results Regression Variable Results

Variable

Value

Standard Error t-ratio

Prob(t)

Input Variable

a

0.259

0.015

16.81

0.0

Saturates

b

1.605

0.098

16.43

0.0

Resins

c

17.198

2.215

7.76

0.0

A/R

d

0.050

0.003

19.17

0.0

ln viscosity

e

0.002

0.000

8.63

0.0

Resins

f

0.001

0.000

4.54

0.00001

Asphaltenes

g

8.505

3.348

2.54

0.01

A/R

h

1.159

0.143

8.11

0.0

ln viscosity

i

0.700

0.170

4.11

0.00004

Resins

j

2.971

0.386

7.70

0.0

A/R

k

0.00000006 0.000

10.90

0.0

ln viscosity A/R

l

1.956

0.663

2.95

0.00

m

0.000004

0.000

10.24

0.0

Exp density

n

0.000146

0.000

2.70

0.01

A/R

1.600

7.69

0.0

o

12.305

Math Applied

Cubed

ln

Exp squared

log/ square x

Contant

Resin content: if the value of the resins is zero, then set this value to 20. If the resin content is less than 10, then the resin content parameter is 10 less the resin content, and if it is greater than 10, it becomes the resin content less 10. The value used in the equation is this transformed value. (12) Asphaltene content: if the value of the asphaltene content is zero, then set the value to 20. If the asphaltene content is less than 4, then the asphaltene content parameter is 4 less the asphaltene content, and if it is greater than 4, it becomes the asphaltene content less 4. The value used in the equation is this transformed value. (13) A/R or asphaltene/resin ratio: this is taken as the direct value of the asphaltene content in percent (untransformed) divided by the resin content in percent (again untransformed). If A/R is less than 0.6, then the A/R parameter is

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0.6 less the A/R, and if it is greater than 0.6, it becomes the A/R less0.6. The value used in the equation is this transformed value. (14) The class of the resulting emulsion is then calculated as follows: Stability C ¼ 12:3 þ 0:259 St 1:601 Rt 17:2 A=Rt 0:50 Vt3 þ 0:002 Rt3 þ 0:001 At3 þ 8:51 A=Rt3 1:12 ln Vt þ 0:700 lnRt þ 2:97 lnA=R þ 6:0108 ExplnVt2 1:96 ExpA=Rt2 4:0106 logExpDt=expDt2 1:5104 logA=Rt=A=Rt2

(15)

where Class is the Stability C of the resulting water-in-oil type, St is the transformed saturate content as calculated in Equation (11), abbreviated A here, Rt is the transformed resin content as calculated in Equation (12), abbreviated B, A/Rt is the transformed asphaltene/resin ratio as calculated in Equation (14), abbreviated C, Vt3 is the cube of the transformed ln viscosity as calculated in Equation (10), abbreviated D, Rt3 is the cube of the transformed resin content as calculated in Equation (12), abbreviated E, At3 is the cube of the transformed asphaltene content as calculated in Equation (13), abbreviated F, A/Rt3 is the cube of the transformed A/R ratio as calculated in Equation (14), abbreviated G, lnVt is the natural logarithm (ln) of the transformed viscosity as calculated in Equation (10), abbreviated H, lnRt is the natural logarithm (ln) of the transformed resin content as calculated in Equation (12), abbreviated I, lnA/R is the natural logarithm (ln) of transformed asphaltene/resin ratio as calculated in (14), abbreviated J, ExplnVt2 is the exponential of the transformed viscositydsquareddas calculated in Equation (10), abbreviated K, ExpA/Rt2 is the is the exponential of the A/R ratio e squareddas calculated in Equation (14), abbreviated L, logExpDt/expDt2 is the is the logarithm (base 10) of exponential of the densityddivided by the square of the transformed densityd the transformed density as calculated in Equation (9), abbreviated M, logA/Rt/A/Rt2 is the logarithm (base 10) of exponential of the A/R ratioddivided by the square of the A/R ratiodthe transformed A/R ratio as calculated in Equation (14), abbreviated N.

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Chapter | 10 Models for Water-in-Oil Emulsion Formation

A simplified version of the equation is then: Stability C ¼ 12:3 þ 0:259A 1:601B 17:2C 0:50D þ 0:002E þ 0:001E þ 8:51G 1:12H þ 0:700l þ 2:97J þ 6:0108 K 1:96L 4:0106 M 1:5104 N (16) where the parameters A to N are defined as above. The Stability C of the resulting product is calculated using the rheological measurements of the water-in-oil product formed.2 The basic uncorrected Stability C or cross product is: Xpr ¼ Complex Modulus Starting Oil ViscosityðStability AÞ Elastic Modulus Starting Oil ViscosityðStability BÞ

(17)

The corrected stability C is: Stability C ¼ In ðXpr XprÞ=1000

(18)

where the Xpr is the value from Equation (17). Or, equivalently, the Stability C can be calculated from: Stability C ¼ InðStab A Stab BÞ2 =1011

(20)

The values of Stability C that are assigned to each class are given in Table 10.3. The viscosity of the resulting product can be taken as the average of the types at a given time as shown in Table 10.4.

TABLE 10.3 Conditions for Type Calculations Calculated C Stability Minimum

Maximum

2.2

Other Conditions

State

Error (%)

15

Stable

0

12

0.7

Mesostable

9

18.3

9.1

density >0.96 viscosity > 6000

Entrained

7

7.1

39.1

density <0.85 or >1.0 viscosity<100 or >800000 Asphaltenes or Resins <1%

Unstable

10

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TABLE 10.4 Viscosity Increases From Starting Oil Viscosity Increase On First Day

Week

Year

Entrained

1.9

1.9

2.1

Mesostable

7.2

11

32

Stable

405

1054

991

Unstable

0.99

1.0

1.0

10.5. DEVELOPMENT OF AN EMULSION KINETICS ESTIMATOR The kinetics of emulsion formation have been studied, and data are available to compute the time to formation.45,46 This kinetics study has shown that the times to formation for stable emulsions is particularly rapid and that of entrainment is also rapiddboth in a matter of minutes. This study yielded data in terms of relative formation time and energy (rpm) of the mixing apparatus. A study in a large test-tank has also yielded data on the formation time of the various water-in-oil states. The data of the relative formation times and the wave height are available. The average data over 25 runs were used to calculate the formation time, which was taken as that time at which 75% of the maximum stability measured occurs. The conditions under which these tests took place and the measurements taken are described in the literature.55,56 The wave height for each experiment was measured and used to indicate relative sea energy, taken for a fully developed sea. The laboratory data was converted from relative rotational energy to wave height by equating formation times and then using this multiplier to calculate the equivalent wave height. Formulae were fitted to each of the three categories, and the common formula among all three relevant classes was found to be 1/x1.5 (24), as shown in Table 10.5. The regression coefficients for this formula are also given. Application of the equations in Table 10.5 will then provide a user with a time to formation of a particular water-in-oil state, given the wave height.

10.6. DISCUSSION The first point to be discussed here will be the error or goodness-of-fit. Table 10.2 shows the specific regression statistics by fit parameter. The first column is the value that is used in the predictor equations. The data in the Standard Error column are the estimates of the standard deviations of the fitted regression parameters. The third column is the t-ratio, which is the ratio of the estimated parameter value to the estimated parameter standard deviation. The larger the ratio is, the more significant the parameter is in the regression model. This is

261

Chapter | 10 Models for Water-in-Oil Emulsion Formation

TABLE 10.5 Wave Height Prediction equation y ¼ a þ b/x1.5(24)

Resulting Equation Predictor

a

b

R2

Stable

27.1

7520

0.51

Mesostable

47

49100

0.95

Entrained

30.8

18300

0.94

x is wave height in cm y ¼ time to formation in minutes

a test statistic to determine if the actual parameter value is zero. If the value were zero, then it should not be included in the equation. As can be seen in Table 10.2, the t-ratios are very high; thus, all remaining input parameters are valid. This exercise had begun with 30 input parameters, and only 14 remained that are highly significant. The fourth column is the Prob(t), which is the probability that a parameter can be dropped without losing significance to the model. If Prob(t) ¼ 0.95, there is a 95% chance that the actual parameter value is zero. In cases like the latter, the parameter in question can usually be removed from the model without affecting the regression accuracy. As can be seen from Table 10.2, all the 14 values are highly significant and should not be dropped. Table 10.3 shows the error percentage by water-in-oil type. There is little error for more stable types, but more error for the unstable types. This was noted in past modeling as well.45,46 It is suspected that the reasons for this are as follows: 1. Unstable types generally consist of three widely separate classes of oils or fuels, very light oils such as the fuels that have little or no resins or asphaltenes; those very heavy oils that are so viscous that they will not uptake water; and those oils that have the incorrect ratio or amounts of resins or asphaltenes. It is difficult to mathematically incorporate all three of these variances into one grouping. 2. Some of the oils may be able to form different water-in-oil types, but emulsion inhibitors or asphaltene suspenders have been added to the products. These types of oils make prediction very difficult as they mislead the regression process. 3. There are many different asphaltenes, some of which make much more stable emulsions than other. Recent work has shown that there are hundreds of asphaltene subcomponents varying very much in composition and molecular size.14 Thus the percent of asphaltenes (or resins) certainly does not tell the whole story about the emulsion-stabilizers in the emulsions. It is important to recognize the properties and behaviors of the four waterin-oil types; they are very different from each other. Table 10.6 shows the

262

TABLE 10.6 Average Properties of the Four Water-in-Oil Types Rheological Properties on Day of Formation Viscosity Increase From Starting

Complex Modulus (mPa)

Elasticity Modulus (mPa)

tan delta (V/E)

Complex Viscosity (mPa.s)

Appearance

Entrained

Viscous Black

44.5

27.5*

1.9

829750

514010

1.73

132305

Mesostable

Viscous Reddish

64.3

29.6*

7.2

132780.5

107294.5

1.7

21219.9

Stable

Solid Reddish

80.7

77.4

405

749991.4

710445.9

0.7

119683.2

Unstable

Oil-Like

6.1

6.85*

0.0

10965637.5

3367562.5

2.4

1828062.5

Rheological Properties One Week After Formation

Appearance

Viscosity Increase From Starting

Complex Modulus Change

Elasticity Modulus Change

Tan Delta Change

Complex Viscosity Change

Entrained

Viscous Black

1.9

0.8

0.6

1.7

1.3

Mesostable

Broken

5.4

0.6

0.5

2.2

0.5

Stable

Solid Reddish

859

1.1

1.1

1.1

1.2

Unstable

Oil-Like

0.0

3.8

2.9

1.7

3.6

(Increase is shown as number larger than 1.00) *These water content values are high as most were not measured as they were obviously low.

Behaviour of Oil in the Environment and Spill Modeling

Averages

PART | IV

Week Water Content (%w/w)

Day Water Content (%w/w)

263

Percent Water Loss (Day–1) per Starting Value

Chapter | 10 Models for Water-in-Oil Emulsion Formation 3.0 Entrained Mesostable Stable Unstable

2.5 2.0 Unstable

1.5 1.0 0.5 Stable

0.0

Entrained

Mesostable

e–1

e0 1

e1

e2

7

e3

e4

e5

e6 1 Year

Time (Days) FIGURE 10.3 Graph of water loss as a rate over time for the four water-in-oil types.

different average properties of the four water-in-oil types. It should be noted that on the day of formation, only mesostable and stable may be somewhat similar, this similarity disappears after one week when the mesostable emulsions break down. The change in water content with time is portrayed in Figure 10.3. As can be seen, the unstable type loses the most water in the first few days. The water content itself is shown in Figure 10.4. As the figure shows, the water content of the four types is very different at the start and especially after one week. Figures 10.3 and 10.4 show that the rate of water loss and the amount of water are indeed different aspects of these water-in-oil types. The other significant point in these figures is that the change in water content of the entrained types is the slowest of all. Another comparison of the four water-in-oil types is shown in Figures 10.5 and 10.6 and relates to the viscosities of types. The rate of change in viscosity is quite different for the four types as seen in Figure 10.4. The rise of viscosity for the mesostable types is more than expected and is due to weathering of the oil, which proceeds more rapidly without water content. The apparent viscosity of the four water-in-oil types is shown in Figure 10.5. As the viscosity axis is logarithmic, the differences are very significant between the four types. In summary, the four water-in-oil types differ by as much as orders-of-magnitude in viscosity and water content at one day, one week, and one year. A question that remains is, how good is this model compared to other models? Table 10.7 shows a comparison for several oil types. These oil types were chosen as common types that are in commerce or as they are well known for their properties. The first six columns give the oil identification and its

264

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

80

Stable

70

Mesostable

Water Content %

60

Entrained Mesostable Stable Unstable

50 40

Entrained

30

A

20

Unstable

10 0

e0

e1

e2

e3

e4

e5

e6

ln Time (Days) FIGURE 10.4

Graph of absolute water content of the four water-in-oil types.

Log Rate of Change in Viscosity

3.5 3.0

Stable

2.5

Entrained Mesostable Stable Unstable

2.0 1.5 1.0 Mesostable

0.5

Entrained

0.0 –0.5

Unstable

e-1

FIGURE 10.5

e0 1

e1

e2

e3 7 Time (Days)

e4

e5

1 Year

e6

Graph of the change in viscosity for the four water-in-oil types.

actual properties of water content and viscosity on the first day of formation. The first model compared shows the traditional exponential water uptake as predicted using the equations presented in Section 10.2. The shaded and boxed values are those predictions in error. A criterion was set for at least 25% in

Chapter | 10 Models for Water-in-Oil Emulsion Formation

T

265

D

FIGURE 10.6 Graph of the apparent viscosity of the four water-in-oil types with time.

water content or incorrect typedif predicted. The number of cases predicted correctly are given at the bottom, and the average orders-of-magnitude are different from the actual value. Table 10.7 shows that the old exponential water uptake model predicted the correct water content only 7% of the time in this set of oils and the order of magnitude of the magnitude of viscosity was over two order-of-magnitudes from the actual viscosity on the day of formation. This comparison would indicate that exponential water uptake models are not useful and are, in fact, misleading. The second model compared in Table 10.7 is that of ADIOS, a widely available model from National Oceanic and Atmospheric Administration (NOAA). The water content is predicted within about 25% in about 49% of cases, and the viscosity averages about 1.6 orders-of-magnitude in error from the actual value. The third model compared is the older model by the present author. It predicted the water content correctly in 72% of cases as well the correct water-in-oil type. The last model compared is the one presented for the first time in this chapter. This model was excellent and predicted the water content and type 100% correct for all types and the viscosity error average 0.2 orders-of-magnitude. The prediction capability of the models is illustrated in Figures 10.7 and 10.8. Figure 10.7 shows the percent correctness in assigning state (by water content in the case of the older models). Figure 10.8 shows the ordersof-magnitude in errors for the water content and the viscosity for the models. As can be seen by these figures, the old exponential model predicts so few correctly that it is probably misleading.

266

TABLE 10.7 Comparison of Predictions of Various Models

%

A