Methane mitigation from the oil and gas (O&G) sector represents a key near-term global climate action opportunity. Recent legislation in the United States requires updating current methane reporting programs for oil and gas facilities with empirical data. While technological advances have led to improvements in methane emissions measurements and monitoring, the overall effectiveness of mitigation strategies rests on quantifying spatially and temporally varying methane emissions more accurately than the current approaches. In this work, we demonstrate a quantification, monitoring, reporting, and verification framework that pairs snapshot measurements with continuous emissions monitoring systems (CEMS) to reconcile measurements with inventory estimates and account for intermittent emission events. We find that site-level emissions exhibit significant intraday and daily emission variations. Snapshot measurements of methane can span over 3 orders of magnitude and may have limited application in developing annualized inventory estimates at the site level. Consequently, while official inventories underestimate methane emissions on average, emissions at individual facilities can be higher or lower than inventory estimates. Using CEMS, we characterize distributions of frequency and duration of intermittent emission events. Technologies that allow high sampling frequency such as CEMS, paired with a mechanistic understanding of facility-level events, are key to an accurate accounting of short-duration, episodic, and high-volume events that are often missed in snapshot surveys and to scale snapshot measurements to annualized emissions estimates.

We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas facilities that uses concentration data collected by point-in-space continuous emissions monitoring systems (CEMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and end times (event detection) and pattern matches simulated and observed concentrations to estimate emission source location (localization) and rate (quantification). We test the framework on a month of single-source controlled releases ranging from 0.50 to 8.25 hours in duration and 0.18 to 6.39 kg/hr in size conducted at the Methane Emissions Technology Evaluation Center in Fort Collins, Colorado. All controlled releases are identified and 82% are localized correctly. For emissions < 1 kg/hr, the framework underestimates by 37.2% on average, with 90% of rate estimates within a factor of [-4.6, 2.8] or a percent difference of [-78.1%, 178.6%]; for emissions > 1 kg/hr, the framework overestimates by 1.5% on average, with 90% of rate estimates within a factor of [-2.0, 1.8] or a percent difference of [-49.6%, 77.4%] from the true rates. Potential uses for the proposed framework include near real-time alerting for rapid emissions mitigation and emission quantification for data-driven inventory estimation on production-like facilities.

Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed, site-level emission inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that operator-level top-down methane measurements are lower during the end-of-project phase than during the baseline phase. However, gaps persist between end-of-project topdown measurements and bottom-up site-level inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) validate specific snapshot measurements and determine how they relate to the temporal emission profile of a given site and (ii) create a measurement-informed, site-level inventory that can be validated with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how to reconcile CMS rate estimates and top-down snapshot measurements jointly with bottom-up inventories at the site level. More broadly, it demonstrates the importance of multiscale measurements when creating measurement-informed, site-level emission inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as the Oil and Gas Methane Partnership 2.0, and corporate strategies.

We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas production sites that uses concentration data collected by point-in-space continuous monitoring systems (CMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and end times (event detection) and pattern matches simulated and observed concentrations to estimate emission source location (localization) and rate (quantification). We test the framework on a month of non-blinded, single-source controlled releases ranging from 0.50 to 8.25 hours in duration and 0.18 to 6.39 kg/hr in size. All controlled releases are identified and 82% are localized correctly. 5.5% of predicted events are false positives. For emissions <= 1 kg/hr, the framework underestimates by 37.2% on average, with 90% of rate estimates within a factor of [-4.6, 2.8] or a percent difference of [-78.1%, 178.6%] from the true rate. For emissions > 1 kg/hr, the framework overestimates by 1.5% on average, with 90% of rate estimates within a factor of [-2.0, 1.8] or a percent difference of [-49.6%, 77.4%]. Potential uses for the proposed framework include near real-time alerting for rapid emissions mitigation and emission quantification for data-driven inventory estimation on production sites.

Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed emissions inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that basin- and operator-level top-down measurements show lower methane emissions during end-of-project than during baseline 9-months earlier. However, gaps persist between end-of-project top-down measurements and bottom-up inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) assess the validity of snapshot measurements and determine how they relate to the temporal emissions profile of a given site and (ii) create a near-real time, measurement-informed inventory that can be cross-checked with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how CMS can be used to reconcile top-down snapshot measurements with bottom-up inventories at the site-level. More broadly, it demonstrates the importance of multi-scale measurements when creating measurement-informed emissions inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as OGMP 2.0, and corporate strategies.