Assessment of Methane Emission Source Characteristics from Oil and Gas Operations Using Satellite, Weather, Environmental, and Operational Data

化石燃料能源部门贡献了全世界当代人为甲烷排放量的约1/3。然而，由于以下几个原因，对来自石油/天然气（O/G）操作的甲烷排放的可靠估计是复杂的：（1）在广泛的地理区域（例如，具有>1000口威尔斯的生产盆地）是不可用的;（2）从大规模监测中识别和量化排放源（例如，卫星）数据是具有挑战性的，因为对于这种不适定的逆问题缺乏鲁棒的分析技术。事实上，越来越多的证据表明，与其他基于大气测量的自上而下的方法相比，涉及当地测量的自下而上的方法往往会导致系统性低估。该项目旨在开发利用卫星反演大气甲烷摩尔分数（XCH 4）的新技术，以量化甲烷排放率，确定排放源，并评估其特征。时间平均XCH 4图，以及其他环境、气象和O/G操作/生产数据（例如，井活动和生产量）来推断潜在的排放源。还将分析有关周边地区其他主要排放源的信息（例如，垃圾填埋场、煤矿和城市住宅）。以往的研究主要集中在跟踪一个或几个特定的排放源的排放，这些技术不适合检查来自大型O/G流域的排放与许多来源。该项目旨在开发数据驱动的建模框架，用于量化O/G作业的甲烷排放源并评估相关特征，具体方法是：（1）构建机器或深度学习模型，将生产数据和井活动与甲烷排放率关联起来;和（2）了解O/G活动与排放源特征之间的关系。这些成果，包括守则、程序和主要产烷盆地的具体案例研究，将有助于评估现有业务的环境绩效和确定进一步的监测机会。

The fossil fuel energy sector has contributed approximately 1/3 of the contemporary anthropogenic methane emissions worldwide. However, reliable estimating of methane emissions from oil/gas (O/G) operations is complicated for several reasons: (1) continual detailed field sampling data over an extensive geographical area (e.g., a producing basin with >1000 wells) is unavailable; (2) identifying and quantifying emission sources from large-scale monitoring (e.g., satellite) data is challenging because there is a lack of robust analysis techniques for this ill-posed inverse problem. In fact, there is increasing evidence that bottom-up approaches involving local measurements would often lead to systematic underestimation, as compared to other top-down approaches based on atmospheric measurements. This project aims to develop new techniques for utilizing satellite retrievals of mole fractions of atmospheric methane (XCH4) to quantify methane emission rate, identify emission sources, and assess their characteristics. Maps of time-averaged XCH4, together with other environmental, meteorological, and O/G operational/production data (e.g., well activity and production volumes), are used to infer potential emission sources. Information about other major emission sources in surrounding areas will also be analyzed (e.g., landfills, coal mines, and urban dwellings). Previous studies focused on tracking emissions from one or a few specific emission sources, and those techniques are not suitable for examining emissions from large O/G basins with many sources. This project aims to develop data-driven modelling frameworks for quantifying methane emission sources from O/G operations and assessing the associated characteristics by (1) building machine- or deep-learning models for correlating production data and well activity to methane emission rates; and (2) understanding the relationships between O/G activities and emission source characteristics. The outcomes, including codes, procedures, and specific case studies of major Canadian-producing basins, would be useful for assessing the environmental performance of existing operations and identifying further monitoring opportunities.