Temporal Analysis of Site-Level Methane Emissions from Nearly One Thousand Upstream Oil and Gas Facilities Equipped with Fixed-Point Continuous Monitoring Systems

David Ball , Ali Lashgari, Nathan Eichenlaub, Noah Metzger

Temporal variability in methane emissions from oil and gas facilities may significantly impact the accuracy of measurement-based emissions inventories and the effectiveness of measurement-based mitigation policies. Yet the existing knowledge of duration, frequency, and magnitude of emission events remains very limited. A deeper understanding of these temporal characteristics is therefore essential for designing monitoring strategies, interpreting top-down assessments, and prioritizing mitigation actions. This study analyzes methane emissions from 940 upstream oil and gas production facilities equipped with fixed-point continuous monitoring systems (CMS) across seven U.S. basins to investigate site-level emission rate distributions and anomalous emission event durations. Site-level emissions are estimated every 15 minutes using a recursive Bayesian inversion that assimilates in situ methane and meteorological measurements, yielding approximately 24.7 million rate estimates. We employ a facility-specific rolling baseline calculation and apply rate thresholding to identify discrete emission events (emissions that significantly exceed a slowly-varying temporal site-specific baseline), which are then characterized in terms of event-average rate and duration. Across all facilities, approximately 80–90% of estimated methane mass originates from periods when emissions are below 100 kg/hr, indicating that non–super-emitting conditions dominate cumulative mass budgets. The facility-level emissions distribution is highly skewed, with 10.6% of sites responsible for 50% of total inferred mass. Although only 3.7% of emission rate estimates are classified as event operational states, these events account for 41% of total emissions, with the majority of event mass arising from intermittent, moderate-rate anomalies.