Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH4 emissions at different spatial scales are ~1.5–2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH4 mitigation policy priorities.
Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics’ Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1,400 kg/h. When emission rate estimates were generated using wind from high-resolution rapid refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger’s stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), the accuracy of individual emission rate estimates produced using HRRR wind ranged from −64.1% to +87.0%. Across all controlled releases, 38.1% of estimates had error within ±20%, and 87.3% of measurements were within a factor of two (−50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h), emission estimates are biased high, however when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications that must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models or to audit reported emission levels from oil and gas systems.
Methane (CH4) from oil and gas (O&G) activities is a known contributor to global anthropogenic methane emissions and recent research has demonstrated that a small fraction of large emitters contribute to the majority of total emissions. In this study, we perform a single-blind evaluation of the quantification capabilities of three airplane-based technologies (Bridger Photonics’ Gas Mapping LiDAR, Carbon Mapper’s Global Airborne Observatory, and GHGSat-AV) with a focus on large emitters (10-2,000+ kg h-1 CH4). In two 2021 campaigns, metered natural gas was released concurrently with overpasses by the tested technologies. Results were submitted by operators in a three-stage unblinding process. All teams detected 100% of releases above 50 kg h-1 CH4. The teams report parity slopes of 0.35 to 1.06, with R2 values of 0.35 to 0.78. After 10-meter anemometer wind measurements were unblinded, two out of three teams significantly reduced variance in the parity slope, highlighting the importance of accurate wind data. After half of metered release volumes were subsequently unblinded, improvement was mixed. These results suggest that multiple commercially available technologies can reliably detect larger point-source methane emissions, with varying quantification performance and trade-offs between survey area coverage and instrument sensitivity.
Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States (US), recent synthesis studies of field measurements of CH4 emissions at different spatial scales are ~1.5x-2x greater compared to official Environmental Protection Agency (EPA) greenhouse gas inventory (GHGI) estimates. Site-level field studies have isolated the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH4 emissions using bootstrap resampling that agrees within error with recent syntheses of site-level field studies and allows for isolation of differences between our inventory and the GHGI at the equipment-level. We find that venting and malfunction-related emissions from tanks and other equipment leaks are the largest contributors to divergence with the GHGI. To further understand this divergence, we decompose GHGI equipment-level emission factors into their underlying component-level data. This decomposition shows that GHGI inventory methods are based on measurements of emission rates that are systematically lower compared with our updated synthesis of more recent measurements. If our proposed method were adopted in the US and other jurisdictions, inventory estimates could become more accurate, helping to guide methane mitigation policy priorities.
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