Publications

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h−1 from 188 platforms; 118 kg h−1 from 104 platforms using mass balance methods; 284 kg h−1 from 151 platforms using aircraft remote sensing; and 19,088 kg h−1 from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies.

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane (CH4) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind CH4 concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the CH4 concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments.

This work presents a mechanistic modeling approach for simulating methane emissions from tri-ethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (< 0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.

Current MMRV solutions have the potential to quickly survey entire oilfields or detect methane leaks down to the component level, but also carry high price tags or, indirectly, high implementation costs. The Stanford/EDF Mobile Monitoring Challenge (MMC) conducted in 2018 was the first study to systematically evaluate methane mitigation technologies for incorporation into LDAR programs at the operator level. Three vehicle-based solutions tested in the MMC utilized a fence-line screening pattern that encompassed a production site and equipment, which we refer to as a “drive-around survey,” and showed promising results of greater than or equal to 88% true positive source identification rates for controlled releases in the 0-26 kg CH4/hr range. In this work, we evaluate a similar on-site drive-around survey as an alternative methane leak detection method under the EPA’s recent update to the Standards of Performance for New, Reconstructed, and Modified Sources and Emissions: Oil and Natural Gas Sector (NSPS). We find that a simple methane enhancement threshold binary classification system performs well with true positive rates > 0.8, though the precision of this classifier is inversely related to the magnitude of the emission rates for each class. We also describe a heuristic approach to estimating dispersion without source distance information. Incorporating this information into a linear model of emission rates regressed on survey data, we improve the model fit to R^2 > 0.9.

To align with climate initiatives, multiple reporting programs are transitioning from generic activity-based emission factors to site-specific measured emissions data to estimate greenhouse gas emissions at oil and gas facilities. This study contemporaneously deployed two top-down (TD) aerial methods across 14 midstream facilities, extending work done in Brown et al.1. Methods produced multiple whole-facility estimates at each facility, resulting in 773 individual paired estimates (same facility, same day), and robust mean estimates for each facility. Mean estimates for each facility, aggregated across all facilities, differed by nearly 2:1 (49% [32% to 68%]). At 6 of 14 facilities, the methods produced mean estimates that differed by more than a factor of two. These data suggest that one or both methods did not produce accurate facility-level estimates, at a majority of facilities and in aggregate across all facilities. Overall results are augmented with two case studies where TD estimates at two pre-selected facilities were coupled with comprehensive onsite measurements to understand factors driving the divergence between TD and bottom-up (BU) emissions estimates. In 3 of 4 paired comparisons between the intensive onsite estimates and one of the TD methods, the intensive onsite surveys did not conclusively diagnose the difference in estimates. In these cases, our work suggests that the TD methods mis-estimate emissions an unknown fraction of the time, for unknown reasons. While two methods were selected for this study, it is unlikely that the issues identified here are confined to these two methods; similar issues may exist for other similar whole-facility methods, on midstream and/or other facility types. These findings have important implications for the construction of voluntary and regulatory reporting programs that rely on emission estimates for reporting, fees or penalties, or for studies using whole-facility estimates to aggregate TD emissions to basin or regional estimates.

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emission from production platforms measured using downwind dispersion methods were 32 kg h-1 from 188 platforms; 118 kg h-1 from 104 platforms using mass balance methods; 284 kg h-1 from 151 platforms using aircraft remote sensing; and 19,088 kg h-1 from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depth moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantifications methodologies.

Pipelines transport natural gas (NG) in all stages between production and the end user. The NG composition, pipeline depth, and pressure vary significantly between extraction and consumption. As methane (CH4), the primary component of NG is both explosive and a potent greenhouse gas, NG leaks from underground pipelines pose both a safety and environmental threat. Leaks are typically found when an observer detects a CH4 enhancement as they pass through the downwind above-ground NG plume. The likelihood of detecting a plume depends, in part, on the size of the plume, which is contingent on both environmental conditions and intrinsic characteristics of the leak. To investigate the effects of leak characteristics, this study uses controlled NG release experiments to observe how the above-ground plume width changes with changes in the gas composition of the NG, leak rate, and depth of the subsurface emission. Results show that plume width generally decreases when heavier hydrocarbons are present, the leak rate is reduced, and as leak depth decreases from 0.9 to 0.6 m. The study illustrates that heavy gas leaks from flowlines and gathering lines could currently be more challenging to detect than leaks from distribution pipelines as the plume is trapped close to the ground. Further, this study shows that leaks from pipelines laid in covers meeting minimum depth requirements of 0.9 m could be easier to detect compared to those buried shallower. Overall, this study illustrates that leak survey protocols for flowlines and gathering lines should be different from distribution pipelines and tailored to the compositions of the transported NG to report emissions accurately. The study aims to improve the understanding of flowline/gathering line leaks to prevent fatal accidents, such as the 2017 explosion from a gathering line in Firestone, Colorado, that resulted in two fatalities and one injury.

A proper understanding of subsurface gas migration patterns resulting from belowground natural gas (NG) infrastructure is essential for safe and efficient first responder operations and to improve leak repair actions. Subsurface migration of leaked gas occurs predominately by pressure-driven advection close to the leak. Far migration occurs by concentration-driven diffusion and eventually defines the plume's edge. Therefore, proper formulation of the diffusion behavior in mathematical models of gas transport is critical to safety and response. This study evaluated six diffusivity parametric functions (DPFs) for an accurate representation of soil gas diffusivity through soils by comparing predicted and measured values for mineral, clay, and sand soils. DPFs were further tested by implementing them in a multiphase flow model previously developed for belowground NG transport. DPFs were also tested by comparing with field scale NG leakage experiments conducted in varying soil conditions (mineral soil, sand, and sand/clay) at controlled NG leak rates ranging from 5 - 21 slpm. Results showed that soil-type and structure-dependent DPFs performed better than soil-type and structure-independent DPFs in simulating the diffusive transport of NG. Further, results showed that DPF selection does not significantly influence NG migration simulations under dry soil conditions during the leak period. However, when the leak is terminated, simulation results followed the same trend of over- and under-predictions as of Dp/Do parameterization. Further, wet soil simulations show more than a 10% CH4 composition deviation and 2.5-day time differences among DPFs in reaching explosive limits. Unique to this work, the selection of a DPF can result in large differences in flux estimates in transport models, masking the effects of the gas behavior, especially in cases where gas diffusion to outer boundaries is of interest. The findings, therefore, support the idea that careful selection of DPFs based on soil-type, structure and moisture condition is essential to predict NG plume behavior during a leak event and after repair.

Methane (CH4) emission rates from a sample of natural gas facilities across industry sectors were quantified using the dual tracer flux ratio methodology. Measurements were conducted in study areas within the Fayetteville shale play, Arkansas (FV, Sept–Oct 2015, 53 facilities), and the Denver-Julesburg basin, Colorado, (DJ, Nov 2014, 21 facilities). Distributions of methane emission rates at facilities by type are computed and statistically compared with results that cover broader geographic regions in the US (Allen et al., 2013, Mitchell et al., 2015). DJ gathering station emission rates (kg CH4 hr–1) are lower, while FV gathering and production sites are statistically indistinguishable as compared to these multi-basin results. However, FV gathering station throughput-normalized emissions are statistically lower than multi-basin results (0.19% vs. 0.44%). This implies that the FV gathering sector is emitting less per unit of gas throughput than would be expected from the multi-basin distribution alone. The most common emission rate (i.e. mode of the distribution) for facilities in this study is 40 kg CH4 hr–1 for FV gathering stations, 1.0 kg CH4 hr–1 for FV production pads, and 11 kg CH4 hr–1 for DJ gathering stations. The importance of study design is discussed, including the benefits of site access and data sharing with industry and of a scientist dedicated to measurement coordination and site choice under evolving wind conditions.

The recent regulatory spotlight on continuous monitoring (CM) solutions and the rapid development of CM solutions have demanded the characterization of solution performance through regular, rigorous testing using consensus test protocols. This study is the second known implementation of such a protocol involving single-blind controlled testing of 9 CM solutions. Controlled releases of rates (6–7100 g) CH4/h over durations (0.4–10.2 h) under a wind speed range of (0.7–9.9 m/s) were conducted for 11 weeks. Results showed that 4 solutions achieved method detection limits (DL90s) within the tested emission rate range, with all 4 solutions having both the lowest DL90s (3.9 [3.0, 5.5] kg CH4/h to 6.2 [3.7, 16.7] kg CH4/h) and false positive rates (6.9–13.2%), indicating efforts at balancing low sensitivity with a low false positive rate. These results are likely best-case scenario estimates since the test center represents a near-ideal upstream field natural gas operation condition. Quantification results showed wide individual estimate uncertainties, with emissions underestimation and overestimation by factors up to >14 and 42, respectively. Three solutions had >80% of their estimates within a quantification factor of 3 for controlled releases in the ranges of [0.1–1] kg CH4/h and > 1 kg CH4/h. Relative to the study by Bell et al., current solutions performance, as a group, generally improved, primarily due to solutions from the study by Bell et al. that were retested. This result highlights the importance of regular quality testing to the advancement of CM solutions for effective emissions mitigation.

Continuous emission monitoring (CM) solutions promise to accelerate fugitive methane emission mitigation in natural gas infrastructure by detecting large sources sooner than they would be detected in traditional leak surveys. Quantification by CM solutions has also been proposed as the foundation of measurement-based inventories. Herein we present the first published results of CM solutions tested using a consensus protocol. Single-blind testing was performed with controlled releases at a dedicated facility, using test conditions that were challenging but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicate detection limits (emission rate with 90% probability of detection) of 3-30 kg CH4/h, with 6 of 11 solutions in the range of 3-6 kg CH4/h, with high uncertainty. Of the 11 solutions tested, 6 provided emission rates estimates and for emission rate of 0.1-1 kg/h, the solutions' mean relative errors ranged from -44% to +586% with single estimates between -90% and +300% (all uncertainties stated as empirical 95% confidence intervals) with most solutions' upper uncertainty exceeding +900%. Solutions' mean relative error for emission rates >1 kg/h is -40% to +93% with two solutions achieving mean relative errors below 20%. In this range, all solutions exhibited single-estimate relative error from below -80% to values above +240%. When test results were simulated using emissions data from a portfolio of facilities, two solutions estimated emissions to within ±20%, while 4 estimated emissions at least double the true emission rate. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicate that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting. Additionally, given the rapid development of these solutions, additional implementation standards and regular testing will also be necessary.

Divergence in recent oil and gas related methane emission estimates between aircraft studies (basin total for a midday window) and emissions inventories (annualized regional and national statistics) indicate the need for better understanding the experimental design, including temporal and spatial alignment and interpretation of results. Our aircraft-based methane emission estimates in a major U.S. shale gas basin resolved from west to east show (i) similar spatial distributions for 2 days, (ii) strong spatial correlations with reported NG production (R2 = 0.75) and active gas well pad count (R2 = 0.81), and (iii) 2× higher emissions in the western half (normalized by gas production) despite relatively homogeneous dry gas and well characteristics. Operator reported hourly activity data show that midday episodic emissions from manual liquid unloadings (a routine operation in this basin and elsewhere) could explain ∼1/3 of the total emissions detected midday by the aircraft and ∼2/3 of the west-east difference in emissions. The 22% emission difference between both days further emphasizes that episodic sources can substantially impact midday methane emissions and that aircraft may detect daily peak emissions rather than daily averages that are generally employed in emissions inventories. While the aircraft approach is valid, quantitative, and independent, our study sheds new light on the interpretation of previous basin scale aircraft studies, and provides an improved mechanistic understanding of oil and gas related methane emissions.