Dorit Hammerling: Investigating the ergodic assumption

Abstract

The ergodic assumption in the context of oil and gas methane emissions is often stated as follows: methane measurements at individual points in time across many sites are distributionally equivalent to methane measurements over time on those sites. A number of studies leverage this assumption to create time-averaged (e.g., annualized) inventories using many methane measurements across sites, making it an important assumption to study in detail. However, it is often challenging to test the applicability of this assumption, as doing so requires both dense coverage across sites and in time. As part of a broader measurement campaign in the Appalachian Basin, continuous monitoring systems (CMS) have been installed on dozens of production sites, providing a unique opportunity to investigate the ergodic assumption given the temporal extent of these data. In this talk, we first provide an overview of the ergodic assumption in the context of oil and gas methane emissions. We then investigate the applicability of this assumption using the CMS data collected in the Appalachian basin. Specifically, we highlight the nuances that must be carefully accounted for when using CMS in this context and show initial results using two years of data focusing on three prototypical sites.

Bio

Prof. Hammerling obtained a M.A. and PhD (2012) from the University of Michigan in Statistics and Engineering, followed by a post-doctoral fellowship at the Statistical Applied Mathematical Sciences Institute in the program for Statistical Inference for massive data. She then joined the National Center for Atmospheric Research, where she led the statistics group within the Institute for Mathematics Applied to the Geosciences and worked in the Machine Learning division before becoming an Associate Professor in Applied Mathematics and Statistics at the Colorado School of Mines in January 2019. Prof. Hammerling received the Early Investigator Award from the American Statistical Association, Section on Statistics and the Environment, in 2018, and the Outstanding Associate Professor Award of the College at the Colorado School of Mines in 2024. Before her return to graduate school, Prof. Hammerling worked in industry as an automation engineer and senior quality specialist. She is a founding member and on the technical leadership team of the recently established Energy Emissions Data Modeling Lab, which is focused on creating transparent models, software and datasets for accurate greenhouse gas emissions accounting across global oil and gas supply chains.