Methane Emissions in Oil and Natural Gas Supply Chains

David T. Allen is the Norbert Dittrich-Welch Chair in Chemical Engineering and co-Director of the Center for Energy and Environmental Systems Analysis at the University of Texas at Austin. He has authored over 300 papers and seven books and chaired the U.S. Environmental Protection Agency’s Science Advisory Board. Erin E. Tullos is a Professor of Practice in Chemical Engineering and a senior research fellow at the Center for Energy and Environmental Systems Analysis at the University of Texas at Austin, specializing in methane emissions and atmospheric chemistry with nearly two decades of experience spanning industry, academia, and policy. Arvind P. Ravikumar is co-Director of the Center for Energy and Environmental Systems Analysis and an Assistant Professor at the University of Texas at Austin. He has published over 50 peer-reviewed articles on greenhouse gas emissions and advises state and federal governments on energy supply chain emissions.

• Table of ContentsMethane Emissions in Oil and Natural Gas Supply Chains: A Practical Guide  DedicationAcknowledgementsPrefaceAbout the AuthorsForeword  Chapter 1: The Role of Methane Emissions in Global Warming1.1 Introduction1.2 Greenhouse gases and global warming1.3 Global warming potentials and the potency of greenhouse gases1.4 Defining emissions in units of carbon dioxide equivalents (CO₂e)1.5 The global methane balance and methane emissions from global energy systemsProblemsReferences  Chapter 2: Sources and Characteristics of Methane Emissions from Oil and Gas Sources2.1 Introduction2.2 Natural gas and natural gas liquid (NGL) supply chains2.2.1 Production and processing2.2.2 Transmission and storage2.2.3 Distribution to domestic users2.2.4 Liquefaction, export, shipping, and regasification2.2.5 Other global supply chains2.3 Emissions by supply chain segment2.3.1 Categories of emissions2.3.2 Emissions from the production and processing segment2.3.3 Emissions from the transmission, storage, and distribution segments2.3.4 Emissions from the liquefaction, export, shipping, and regasification segment2.4 Temporal variability in emissions at source, site, and regional levelProblemsReferences  Chapter 3: Source Level Emission Identification and Estimation3.1 Introduction3.2 Identifying sources3.3 Source level emission estimates3.3.1 Exhaust from compressors, heaters or other combustion devices3.3.2 Leaks3.3.3 Pneumatic controllers3.3.4 Venting from liquid storage tanks3.3.5 Venting associated with liquid unloadings3.3.6 Application of source level emission estimates at the asset level3.4 SummaryProblemsReferences  Chapter 4: Improving Source Level Inventories4.1 Introduction: Progressive development of emission inventories4.2 Evaluating data quality4.2.1 Core principles4.2.2 Frameworks for data quality assessments4.3 Systematic improvement to source-level estimating methods4.3.1 Case Study of the Fugitive Component Emission Estimates4.3.2 Accounting for variation in equipment design4.4 Conclusion: Systematic improvement through iterationProblemsReferences  Chapter 5: Prediction, Prevention, and Quantification of Large Unintended Emissions5.1 Introduction5.2 Observational data on unintended large emission events5.3 Emission event prediction and prevention through reliability analysis5.3.1 Hazard identification5.3.2 Quantifying risk with fault trees5.3.3 Preventive barriers, mitigative barriers, and bow-tie diagrams5.4 ConclusionProblemsReferences  Chapter 6: Emission Measurement Systems6.1 Introduction6.2 Source level measurements systems6.2.1 Audio, Visual, and Olfactory (AVO) Inspection6.2.2 Method 216.2.3 Open path, laser-based inspection6.2.4 Optical Gas Imaging (OGI)6.2.5 High-flow measurement systems6.2.6 Stack testing6.2.7 Process-based parameter monitoring and inference6.2.8 Summary of source level measurement methods6.3 Site level measurements6.3.1 Fixed Point Measurement Systems6.3.2 Path Integrated Measurement Systems6.3.3 Fixed Optical Gas Imaging (OGI) Systems6.3.4 Vehicle Mounted Instrumentation with Tracer Release6.3.5 Vehicle Mounted Sensor Without Tracer (EPA OTM 33A)6.3.6 Vehicle-Mounted Passive Systems6.3.7 Aircraft-Based In Situ Measurement Systems6.3.8 Aircraft with Active Systems6.3.9 Aircraft-Mounted Passive Remote Sensing Systems (e.g., Hyperspectral Imaging)6.3.10 Satellite Point-Mapping Systems6.3.11 Summary of Site Level Measurement Systems6.4 Multi-Site and Regional Measurement Systems6.4.1 Regional Monitoring Systems Deployed at Ground Level or on Towers6.4.2 Aircraft with In Situ Measurements6.4.3 Satellite Area Mappers6.5 SummaryProblemsReferences  Chapter 7: Measurement Method Selection7.1 Introduction7.2 Common use cases and measurement requirements7.3 Identifying candidate measurement systems7.4 Selecting measurement systems for source-level inventory improvement7.4.1 Use case 1: Characterizing operational-mode frequency through inspections7.4.2 Use case 2: Refining emission rates for known sources or operating modes7.4.3 Use case 3: Resolving emission duration7.5 Selecting measurement systems to identify previously unrecognized sources or operating modes7.6 Selecting measurement systems to evaluate regional systematic bias7.7 Synthesis: Measurement selection as an iterative inventory improvement processProblemsReferences  Chapter 8: Integrating Measurements into Emission Inventories8.1 Introduction8.2 Measurement integration and reconciliation8.3 Framing questions for using empirical information8.4 Integrating operational data8.5 Additional source-level integration8.6 Site-level integration8.7 Interpreting discrepancies and closing the inventory loopProblemsReferences  Chapter 9: Case Studies of Measurement Integrated Inventories9.1 Introduction9.2 Improving an asset-level inventory9.2.1 Case study of upstream MII with limited operational data9.2.2 Case study of upstream MII with more complete operational data9.2.3 Limited-information midstream operator case9.3 Evaluating the quality of an inventory9.3.1 Validation or verification of a current inventory9.3.2 Validation of an inventory’s continued performance over time9.4 MII development for benchmarking9.4.1 Case study of a regional MII9.5 Synthesis and transition to life cycle assessmentReferences  Chapter 10: Introduction to Supply Chain Emission Analyses10.1 Introduction10.2 Frameworks for Life Cycle Assessment (LCA) and Carbon Footprint of Products(CFP)10.3 Goal and scope definition10.4 Inventory analyses10.5 Impact assessment10.6 Interpretation10.7 Case study of the impact of LCA methodology choices in a CFP analysis10.8 Summary and Use CasesProblemsReferences  Chapter 11: Design Choices in Supply Chain Assessments11.1 Introduction11.2 Functional units and system boundaries11.3 Allocation methods11.4 Mass tracking approaches11.5 An Introduction to Consequential Life Cycle Assessment11.6 Case study of natural gas CFP using site-specific inventor11.7 Integrating measurements into CFPProblemsReferences  Chapter 12: Integrating Source-, Site-, and Region-Scale Measurements into LCA and CFP12.1 Introduction12.2 Framework for Integrating Measurements into CFP12.3 Design Choices in CFP when Integrating Measurements12.4 Use Cases Illustrating Integration of Measurements into CFP12.4.1 Use case – Improve representation of upstream assets by integrating site-scalemeasurement data within CFP12.4.2 Use case – Improve representation of assets by integrating site-scalemeasurement data within CFP12.4.3. Use case – Global benchmarking of suppliers using region-scale measurement data12.5 Uncertainty Estimation in CFP12.5.1 Sensitivity Analysis12.5.2 Monte-Carlo Analysis12.6 LCA harmonizationProblemsReferences  Appendix: Use of Atmospheric Models in Emission Measurement SystemsA.1 Dispersion modelsA.2 Use cases for dispersion modelsA.2.1 Establishing detection limitsA.2.2 Sensor placementA.2.3 Use of dispersion models to infer emissions based on downwind ground measurementsA.2.4 Comparing measurement systemsA.2.5 Choosing a dispersion model and accuracies of dispersion modelsA.3 Chemical transport modelsA.3.1 Regional mass balance measurementsA.3.2 Assigning detection limits and emission estimates to plumes identified by satellitesA.3.3 Estimating emissions in area mapping satellite measurement systems  GlossaryAcronyms