Total Emissions Estimation for Oil and Gas Production Facilities Using Aerial Gas Mapping LiDAR

Abstract We present Gas Mapping LiDAR (GML) for methane emissions monitoring, mitigation, and informatics for oil and gas infrastructure. GML is deployed on small-manned aircraft for periodic monitoring to achieve high-accuracy and cost-effective detection, localization, and quantification of methane emissions. GML combines spatially scanned topographic and methane concentration LiDAR measurements with navigation data and aerial photography to create geo-registered methane plume imagery, LiDAR topography, and photographic mosaics. Data and results based on several years of single- and double-blind controlled release testing conducted with academic and industry partners is presented. Results demonstrate the high sensitivity LiDAR measurements allow detection of methane emissions at rates below 1.5 kg/h with greater than 90% probability of detection in most deployment conditions, allowing the detection of more than 90% of emissions from typical oil and gas production basins. The high spatial resolution of the LiDAR scans provides geo-location of emission sources, typically to within 2 m, for targeted LDAR response. The LiDAR methane concentration measurements, coupled with gas flow speed information, enable single-pass quantification error ranging from −65.6% to +78.9% (95% CI). However, when multiple plume measurements are aggregated from a set of facilities, total emissions rate quantification error below ±10% is achievable, supporting the development of accurate measurement-informed inventories. We present a method for estimating total emissions that combines models for interpreting individual source measurements and infrastructure data in a statistical analysis that is stratified by equipment type and facility class. We demonstrate the method by applying it to aggregated and anonymized emissions data from nine scans of 1350 facilities in the Permian basin and 320 facilities in the Eagle Ford shale play, spanning 2022 to 2023. Back-to-back scans of the facility sets are analyzed to evaluate the repeatability of the total emissions estimates relative to the uncertainty estimates produced by the method. A time series of total emissions from the two facility sets highlights the emissions reductions impact and cost-effectiveness of GML LDAR programs. In the Permian basin, annualized total emissions uncertainty for the scanned facilities was 24% in 2022 and 25% in 2023 with a year-over-year emissions reduction of 30% [7%, 54%] (95% CI). For the Eagle Ford shale play facilities, the annualized total emissions uncertainty was 13% in 2022 and 10% in 2023, with a year-over-year reduction of 20% [5%, 35%] (95% CI).