Bailey Fosdick: Extrapolation Approaches for Creating Comprehensive Measurement-Based Methane Emissions Inventories

Abstract

Measurement-based methane emissions inventories are essential for U.S. oil and natural gas operators to track their progress toward emissions targets, demonstrate the impact of improved operational and monitoring practices, and enable the scientific community to monitor methane budgets at various scales. However, translating raw emission measurement data, whether from continuous monitoring systems, aerial flyovers, or operational cause analyses, into emissions inventories is nontrivial, and advertised inventory numbers often lack any information about the methodology used to produce them. In this talk, we introduce the concept of a comprehensive measurement-based emissions inventory, which represents all emissions across the entire time frame, across all spatial assets, and of all emission sizes for the target scope. We carefully characterize some of the extrapolation efforts necessary to create a comprehensive emissions inventory estimate with data from either continuous monitoring systems or aerial flyover measurements. Understanding these methods is essential to creating defensible emissions inventory reports that adhere to reporting frameworks such as Veritas and the Oil and Gas Methane Partnership 2.0. We provide simple examples to illustrate the sensitivity of annual emissions estimates to the various extrapolation approaches and highlight the challenges, strengths, and limitations when working with data from each of the technologies.

Bio

Bailey Fosdick is an Institute Data Scientist and Senior Manager at GTI Energy, where she leads cross-functional initiatives applying statistical modeling, data science, and artificial intelligence to complex challenges in the energy sector.  She plays a central role in GTI’s Digital Innovation team, contributing to strategic planning across methane emissions research and low-carbon transportation solutions. Before joining GTI Energy, Bailey was an Associate Professor in the Department of Statistics at Colorado State University and the University of Colorado, Anschutz Medical Campus, where she led a research group developing statistical methods for analyzing network data. She holds a PhD in Statistics from the University of Washington.