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.

翻译：减少油气行业的甲烷排放是短期气候行动的关键组成部分。减排工作通常在单个站点层面开展，在此层面，将排放量在有限数量的潜在排放设备之间进行分配，对于泄漏检测与修复以及年化排放的监管报告至关重要。我们提出了一种层次贝叶斯模型，称为多源检测、定位与定量（MDLQ）模型，用于利用点传感器网络的甲烷测量数据对油气站点进行源解析。MDLQ模型考虑了传感器数据中的自相关性，并通过尖峰-平板先验对排放速率估计施加稀疏性约束，因为油气设备通常间歇性排放。我们使用MDLQ模型对一个旨在按已知量释放甲烷的实验性油气站点进行甲烷排放源解析，从而提供模型评估的手段。该实验数据在规模（即受控释放的数量）上具有独特性，且其排放特性与实际油气站点高度吻合。因此，本研究提供了使用点传感器网络在实际运行站点时可预期的源解析精度基准水平。