Recurrent High-Resolution Satellite Observations Quantify Facility-Resolved Annual Large-Emitter Methane Emissions in the Permian Basin
Year: 2026
Type: preprint
Abstract: Repeated high-resolution satellite observations can identify large methane emissions from individual oil and gas facilities, but sparse sampling, intermittent emissions, and variable observation conditions complicate the estimation of annual facility-level emissions. We develop an empirical hierarchical Bayesian method to estimate facility-resolved annual large-emitter methane emissions, with uncertainty, from repeated satellite plume detections and non-detections that were attributed to individual facilities. We apply the method to approximately 100,000 upstream and midstream facilities in the Permian Basin observed by the GHGSat satellite constellation from 2023 through 2025. Mean annual large-emitter emission rates differed strongly by facility class, averaging approximately 0.46 kg h⁻¹ for wells, 32–36 kg h⁻¹ for compressor/gathering stations, and 139–144 kg h⁻¹ for gas processing plants. Mean large-emitter persistence was approximately 0.001 for wells, 0.089–0.091 for compressor/gathering stations, and 0.238–0.249 for gas processing plants. Facility-level estimates were moderately heavy-tailed, with approximately 30–35% of midstream facilities accounting for 50% of estimated annual large-emitter emissions. Class-level mean estimates varied little between years, and more than 98% of matched midstream facilities showed no interannual change exceeding the combined estimation uncertainty. Summed and extrapolated facility estimates for the considered facility classes yielded GHGSat-derived Permian Basin totals of 1.70, 1.66, and 1.63 Mt y⁻¹ for 2023, 2024, and 2025, respectively. These results demonstrate that recurrent satellite observations can support facility-resolved, uncertainty-quantified estimates of the annual contribution from large methane emission events. The framework complements ground and aerial measurements, regional atmospheric inversions, and bottom-up inventories by providing repeated, attributable measurements across large facility populations.
DOI: 10.31223/x5p79f
Institution: GHGSat (Canada)
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7204745368