Geostatistical Inverse Modeling of Methane Area Source Emissions from Oil and Gas using MethaneAIR Observations

Reducing anthropogenic methane (CH₄) emissions is an effective way to combat global warming in the near-term, due to its high global warming potential and relatively short atmospheric lifetime. Mitigation strategies must first be informed by comprehensive monitoring, making methods development for emission quantification a top priority for the scientific community. The top contributor to anthropogenic methane emissions in the United States is the oil and gas (O&G) industry. Losses from aging infrastructure, incomplete combustion during flaring, and operational errors all contribute to CH₄ losses that both contribute to climate change and hurt the producer financially. “Top-down” methods for quantifying CH₄ emission rates combine atmospheric measurements, whether they be satellite-, airborne- or ground-based, with transport models, to quantify these fluxes. MethaneSAT, a hyperspectral satellite slated for launch in fall 2023, will be able to collect data with high spatial and spectral resolution, sensitivity, and a large swath, allowing for simultaneous quantification of point sources and area fluxes via top-down methods. Harvard’s MethaneAIR system, a novel remote sensing platform that serves as an airborne proxy for MethaneSAT, has been shown to be able to detect and quantify individual CH₄ plumes (Chulakadabba et al., 2023, preprint, doi.org/10.5194/egusphere-2023-822; Chan Miller et al., in prep.) Recent work has focused on quantifying emissions for entire O&G production basins, in support of MethaneSAT’s goal of mapping 80% of onshore O&G production in the U.S. We use a geostatistical inverse modeling (GIM) approach with simulated particle footprints from the Stochastic Time-Inverted Lagrangian Transport model (STILT), using a Markov chain Monte Carlo algorithm to solve for the posterior emissions estimates. Here we present the results for multiple O&G basins in the CONUS and discuss how this approach differs from the “classical Bayesian” approach commonly used. This work lays the foundation for future analyses of data from this platform and others, including MethaneSAT, as applied to O&G producing regions, urban areas, agricultural regions and landfills, helping to advance effective CH₄ emissions regulation and policy.