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Evan Sherwin: Incorporating aerial surveys into methane emissions inventories

Event Details:

Thursday, January 13, 2022

Abstract 

Aerial and satellite-based methane surveys have discovered numerous large point-source methane emissions from the oil and gas system. Evidence suggests that some fraction of these emissions are not accounted for in the existing national emissions inventories, which are generated from “bottom-up” compilations of specific emissions sources. To understand the precise contribution of these newly-discovered aerially visible emissions to total emissions from oil and gas production, we introduce a method that combines comprehensive hyperspectral aerial surveys with detailed bottom-up emissions simulations to estimate the contributions from sources that cannot be reliably seen from the “top down” aircraft surveys. We apply the method to the New Mexico Permian Basin and California’s San Joaquin Basin, covering approximately 90% of all oil and gas wells in each case. We find that aerially visible emissions constitute the bulk of total estimated emissions in the New Mexico Permian, substantially greater than in the emissions simulation. In the San Joaquin Basin survey, aerially visible emissions still constitute a substantial fraction of the total. Further, aerially visible emissions, present at only 1-2% of site visits, follow a power law size distribution, with the simulated distribution quickly falling off for emissions below the aerial minimum detection threshold. The resulting distribution generated by combining simulations and surveys largely explains the results of major existing measurement campaigns with different methods. Thus, adopting or approximating this method would likely improve the accuracy of existing national, regional, and global emissions inventories. In addition, these results may assist regulatory assessments of equivalence between emissions reduction approaches, by providing an emissions distribution that accounts for aerially detectable emissions. 

Bio

Dr. Evan Sherwin is a data-informed energy policy analyst investigating the role of hydrocarbon fuels in a rapidly decarbonizing economy. Much of his research focuses on leveraging emerging technologies and datasets to find and fix methane emissions across the oil and gas value chain. Evan is a Postdoctoral Research Fellow at Stanford University’s department of Energy Resources Engineering with a PhD in Engineering and Public Policy and an MS in Machine Learning from Carnegie Mellon University. Evan is the founder and chair of the Methane Emissions Technology Alliance international seminar series and a founding member on the Board of Directors of Climate Change AI. 

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