Estimating Methane Emissions from the Upstream Oil and Gas Industry Using a Multi-Stage Framework

TL;DR

This paper introduces a statistically rigorous, multi-stage sampling framework for estimating methane emissions from oil and gas facilities, providing estimators, variance analysis, and an R package for practical application.

Contribution

It offers a novel multi-stage sampling approach with variance estimators and an efficient modification, addressing the lack of statistical guidance in methane survey design.

Findings

Applied method to aerial survey data in British Columbia

Provided variance estimators for each sampling stage

Developed an R package for implementation

Abstract

Measurement-based methane inventories, which involve surveying oil and gas facilities and compiling data to estimate methane emissions, are becoming the gold standard for quantifying emissions. However, there is a current lack of statistical guidance for the design and analysis of such surveys. The only existing method is a Monte Carlo procedure which is difficult to interpret, computationally intensive, and lacks available open-source code for its implementation. We provide an alternative method by framing methane surveys in the context of multi-stage sampling designs. We contribute estimators of the total emissions along with variance estimators which do not require simulation, as well as stratum-level total estimators. We show that the variance contribution from each stage of sampling can be estimated to inform the design of future surveys. We also introduce a more efficient modification of the estimator. Finally, we propose combining the multi-stage approach with a simple Monte Carlo procedure to model measurement error. The resulting methods are interpretable and require minimal computational resources. We apply the methods to aerial survey data of oil and gas facilities in British Columbia, Canada, to estimate the methane emissions in the province. An R package is provided to facilitate the use of the methods.