A Bayesian hierarchical model for methane emission source apportionment

TL;DR

This paper introduces a hierarchical Bayesian model called MDLQ for accurately apportioning methane emissions from oil and gas sites using sensor data, accounting for intermittency and autocorrelation, and validated with experimental data.

Contribution

The paper presents a novel Bayesian hierarchical model for methane source apportionment that improves accuracy by modeling intermittency and data autocorrelation, validated on experimental site data.

Findings

Model effectively apportions methane emissions in controlled experiments.

Sensor data autocorrelation is explicitly modeled, enhancing accuracy.

Provides a baseline for expected apportionment performance on real sites.

Abstract

Reducing methane emissions from the oil and gas sector is a key component of short-term climate action. Emission reduction efforts are often conducted at the individual site-level, where being able to apportion emissions between a finite number of potentially emitting equipment is necessary for leak detection and repair as well as regulatory reporting of annualized emissions. We present a hierarchical Bayesian model, referred to as the multisource detection, localization, and quantification (MDLQ) model, for performing source apportionment on oil and gas sites using methane measurements from point sensor networks. The MDLQ model accounts for autocorrelation in the sensor data and enforces sparsity in the emission rate estimates via a spike-and-slab prior, as oil and gas equipment often emit intermittently. We use the MDLQ model to apportion methane emissions on an experimental oil and gas site designed to release methane in known quantities, providing a means of model evaluation. Data from this experiment are unique in their size (i.e., the number of controlled releases) and in their close approximation of emission characteristics on real oil and gas sites. As such, this study provides a baseline level of apportionment accuracy that can be expected when using point sensor networks on operational sites.