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

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.

💡 Research Summary

The paper addresses the growing need for accurate, measurement‑based methane inventories for the upstream oil and gas (O&G) sector, focusing on the statistical design and analysis of aerial survey data. Existing approaches rely on a nested Monte Carlo (MC) algorithm combined with a mirror‑match bootstrap to account for sampling error, detection error, and measurement error. While theoretically sound, this method is computationally intensive, difficult to interpret, and lacks open‑source implementation, limiting its practical adoption.

To overcome these limitations, the authors recast the problem as a three‑stage probability sampling design. Stage 1 selects the physical components (e.g., compressors, tanks) to be surveyed from the full set of O&G components in a jurisdiction. Stage 2 selects the days on which each chosen component is observed, reflecting the temporal dimension of emissions. Stage 3 represents each flight pass over a component, where detection (or non‑detection) of methane occurs. By defining first‑order (π_i) and second‑order (π_ij) inclusion probabilities for each stage, they can apply the Horvitz–Thompson (HT) estimator to obtain an unbiased total‑emission estimate:

\