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Methane emissions from the oil and gas sector are a large contributor to climate change. Robust emission quantification and source attribution are needed for mitigating methane emissions, requiring a transparent, comprehensive, and accurate geospatial database of oil and gas infrastructure. Realizing such a database is hindered by data gaps nationally and globally. To fill these gaps, we present a deep learning approach on freely available, high-resolution satellite imagery for automatically mapping well pads and storage tanks. We validate the results in the Permian and Denver-Julesburg basins, two high-producing basins in the United States. Our approach achieves high performance on expert-curated datasets of well pads (Precision = 0.955, Recall = 0.904) and storage tanks (Precision = 0.962, Recall = 0.968). When deployed across the entire basins, the approach captures a majority of well pads in existing datasets (79.5%) and detects a substantial number (>70,000) of well pads not present in those datasets. Furthermore, we detect storage tanks (>169,000) on well pads, which were not mapped in existing datasets. We identify remaining challenges with the approach, which, when solved, should enable a globally scalable and public framework for mapping well pads, storage tanks, and other oil and gas infrastructure. This work uses deep learning on satellite imagery to map well pads and storage tanks in two major U.S. basins. The resulting data fills large gaps in existing databases, a crucial step for improving methane emission estimates and source attribution. [ABSTRACT FROM AUTHOR]

Reducing oil and gas methane emissions is crucially important for limiting the rate of human-induced climate warming. As the capacity of multi-scale measurements of global oil and gas methane emissions has advanced in recent years, including the emerging ecosystem of satellite and airborne remote sensing platforms, a clear need for an openly accessible and regularly updated global inventory of oil and gas infrastructure has emerged as an important tool for characterizing and tracking methane emission sources. In this study, we develop a spatially explicit database of global oil and gas infrastructure, focusing on the acquisition, curation, and integration of public-domain geospatial datasets reported by official government sources and by industry, academic research institutions, and other non-government entities. We focus on the major oil and gas facility types that are key sources of measured methane emissions, including production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. The first version of this global geospatial database (Oil and Gas Infrastructure Mapping database, OGIM_v1) contains a total of ∼ 6 million features, including 2.6 million point locations of major oil and gas facility types and over 2.6×106 km of pipelines globally. For each facility record, we include key attributes – such as facility type, operational status, oil and gas production and capacity information, operator names, and installation dates – which enable detailed methane source assessment and attribution analytics. Using the OGIM database, we demonstrate facility-level source attribution for multiple airborne remote-sensing-detected methane point sources from the Permian Basin, which is the largest oil-producing basin in the United States. In addition to source attribution, we present other major applications of this oil and gas infrastructure database in relation to methane emission assessment, including the development of an improved bottom-up methane emission inventory at high resolution (1 km × 1 km). We also discuss the tracking of changes in basin-level oil and gas activity and the development of policy-relevant analytics and insights for targeted methane mitigation. This work and the OGIM database, which we anticipate updating on a regular cadence, help fulfill a crucial oil and gas geospatial data need, in support of the assessment, attribution, and mitigation of global oil and gas methane emissions at high resolution. OGIM_v1 is publicly available at 10.5281/zenodo.7466757 (Omara et al., 2022a). [ABSTRACT FROM AUTHOR]