Publications

Increased interest in greenhouse gas (GHG) emissions, including recent legislative action and voluntary programs, has increased attention on quantifying, and ultimately reducing, methane emissions from the natural gas supply chain. While inventories used for public or corporate GHG policies have traditionally utilized bottom-up (BU) methods to estimate emissions, the validity of such inventories has been questioned, growing interest in utilizing full-facility measurements using airborne, satellite or drone (top- down (TD)) techniques to inform, improve, or validate inventories. This work utilized full-facility estimates from aerial measurements to evaluate and improve emissions inventories from 15 midstream natural gas facilities in the U.S.A. and represents one of the first systematic studies of measurement-informed inventory methods in this supply chain sector. At each facility, two independent, full-facility estimates were made and compared with a contemporaneous inventory assembled by the facility operator, employing comprehensive inventory methods. Estimates from the two TD methods statistically agreed in 2 of 28 paired measurements. Operator inventories, which include extensions to capture more sources and integrate local measurements, estimated significantly lower emissions than the TD estimates for 40 of 43 paired comparisons. Significant disagreement at most facilities, both between the two TD methods and between the TD estimates and enhanced inventory, indicates that using TD estimates to inform inventories (a) will require additional diagnostic screening and measurement of all sources to improve inventory estimates, and (b) that TD full-facility measurement methods require additional testing, characterization, and potentially improvement, for use in complex midstream facilities.

Top-down studies of methane (CH4) emissions often use the average ethane-to-methane (C2/C1) ratio of wellhead gas in a basin for source attribution between thermogenic and biogenic sources. Biogenic (CH4) sources (ruminants, wetlands, landfills, and other methanogenic sources) do not co-produce ethane (C2H6); the presence of C2H6 indicates a thermogenic CH4 source. However, the C2/C1 ratio often varies within and across basins, as well as among emitting sources and facility configurations. This study uses Fourier Transform Infrared Spectroscopy (FTIR) data from exhaust stack tests of both four-stroke rich-burn and four-stroke lean-burn engines, comparing the relative “destruction efficiency” between the engines and across species. Results show that engines combust heavier hydrocarbons more efficiently than CH4, as evidenced by consistently higher destruction efficiencies for C2H6 across all engine types. The data indicates a preferential destruction of C2H6 (and higher hydrocarbons) relative to CH4; the exhaust gas C2/C1 ratio is consistently lower than the fuel gas C2/C1 ratio. Additionally, recent design modifications at O&G production facilities resulted in a reduction of both C2H6 and CH4 emissions with a more pronounced reduction in C2H6 emissions. In the Denver-Julesburg basin in Colorado, natural gas production increased by 73.9 % from 2015 to 2021. Despite these increases, the companion paper by Daley et al. (2025) reveals that top-down CH4 and C2H6 emissions from oil and natural gas facilities decreased by -25.3%% and 63.6 %, respectively. This work provides a possible explanation and may indicate a shift in emissions from the production sector to the midstream sector.

Standardized controlled testing of emerging methane detection solutions is a critical step in demonstrating emissions mitigation equivalence between emerging solutions and existing regulatory-approved leak detection and repair methods, such as ground-based optical gas imaging (OGI) camera surveys, in the United States and Canada. In this study, 12 solutions─including four hand-held OGI cameras, hand-held NextGen solutions, and mobile solutions (automobile- and drone-based)─were evaluated using a single-blind controlled testing protocol at an outdoor facility designed to simulate emissions from a simplified onshore North American oil and gas production facility. Three solutions were retested 3 to 12 months after the initial assessment using the same protocol and facility to evaluate how performance changed over time. Results indicated that hand-held OGI cameras generally achieved better emission source localization accuracy as well as lower 90% probability of detection and false positive fraction compared to other solution categories. The false negative fractions of OGI cameras were comparable to those of hand-held NextGen solutions but generally lower than those of mobile solutions, which exhibited shorter survey durations relative to other categories of hand-held solutions. The performance of two of three solutions improved with repeat testing, highlighting the potential benefit of regular, comprehensive testing for the development of solutions. Overall, the study findings suggest that while several emerging survey solutions showed promising detection and localization capabilities, hand-held OGI cameras demonstrated higher efficacy in identifying and accurately localizing small emission sources.

Underground natural gas (NG) leaks pose an urgent safety threat, motivating ongoing efforts to improve leak detection methods. The objectives of this study were to investigate how realistic environmental conditions affect methane concentration distributions near leaking underground NG distribution pipelines and ultimately to inform protocols for leak detection by walking surveys. In the first study to do so to date, subsurface and atmospheric methane concentrations were measured at high spatial resolution at a field-scale testbed configured to allow controlled release of NG from an underground source. Our findings demonstrate the importance of considering the effects of subsurface processes with respect to above-ground methane concentrations measured in walking surveys. While subsurface methane concentrations from a large leak (0.52 kg/h of NG, 0.44 kg/h of methane) exceeded 80 vol % 20 cm below the ground, atmospheric concentrations dropped below 100 ppmv (0.01 vol %) within the first 10 cm above the ground when the average wind speed was >2 m/s, demonstrating substantial atmospheric dilution in a narrow boundary layer above the surface under moderate wind conditions. Our analysis indicates that detectors with minimum detection limits on the order of 10 ppmv may be required to detect large underground leaks under certain environmental conditions. While efforts to assess a broader range of leak rates and environmental conditions are ongoing, the findings of this study provide critical insight to practitioners regarding detector performance and placement requirements for walking surveys.

An intensive coordinated airborne and ground-based measurement study was conducted in the Fayetteville Shale in northwestern Arkansas during September and October 2015 to compare and explain potential discrepancies between top-down and bottom-up estimates of regional natural gas (NG) methane (CH4) emissions. In situ mobile downwind measurements are used to document the ethane to methane enhancement ratios (ERs) in emission plumes from NG operations in the region. Enhancement ratios are low (<2% for 87% of NG sources sampled) in this dry gas-producing region and normally distributed around 1.3% in the western half of the study area. A few sampled landfills emitted CH4 but no ethane (C2H6). Sampling drives around large chicken farms, prevalent in the region, did not detect significant downwind CH4 enhancements. In situ airborne measurements of C2H6 and CH4 from area-scale surveys over and downwind of the region documented the resulting ERs from a mix of CH4 sources. Based on these measurements, we show that on average during the measurement windows 85–95% of total CH4 emissions in the western half of the Fayetteville Shale originated from NG sources, which agrees well with bottom-up estimates from the same field study. Lower mixing ratios measured over the eastern half of the region did not support the ER analysis due to the low signal-to-noise on C2H6 measurements.

Continuous Monitoring (CM) solutions have been identified as a method to address methane emissions at oil and gas (O&G) facilities, as these solutions can facilitate faster emission detection and repair compared to traditional survey methods. This study tested 13 CM solutions over 12 weeks using single-blind controlled testing. Controlled release rates ranged from 0.08 to 6.75 kg CH4/hr and lasted 18 minutes to 8 hours. Six solutions demonstrated 90 % method detection limits (DL90s) within the range of controlled releases, from 0.5 [0.3, 0.6] kg CH4/hr to 6.7 [5.9, 8.0] kg CH4/hr. Of the six solutions, four had False Positive (FP) rates between 7.8% and 14.0% (less than 15%), and four had False Negative rates (FN) of 8.0% to 34.1% (less than 50%). Compared with Ilonze et al., these results show retested solutions balancing method sensitivity with FP and FN rates. All scanning/imaging solutions had high localization (≥ 40%) precision and accuracy to the equipment unit. Eleven of the 13 solutions were tested for quantification. Single quantification estimates exhibited high relative quantification errors, outside the -90 % to 1000% range. The mean quantification error ranged from 33 [0.9, 66] %, 95% CI to 1326 [1003, 1648] %, 95 % CI for small emissions, between 0.1 and 1 kg CH4/hr, and from 3 [-20, 26] %, 95% CI to 3578 [-2832, 9988] %, 95% CI for larger emissions, greater than 1 kg CH4/hr. There were 3 and 9 solutions in the small and large emissions categories, respectively, that had less than 100% relative quantification error. Relative to previous studies, errors in quantification estimates decreased, as did FN and FP rates, with improved DL90s for two of the four retested solutions. These findings highlight that continuous, rigorous testing enhances solution performance, with notable improvements observed across multiple testing programs using the same test protocol.

With the increased use of natural gas, safety and environmental concerns from underground leaking natural gas pipelines are becoming more widespread. What is not well understood in leakage incidents is how the soil conditions affect gas migration behavior, making it difficult to estimate the gas distribution. To shed light on these concerns, an increased understanding of subsurface methane migration after gas release is required to support efficient leak response and effective use of available technologies. In this study, three field-scale experiments were performed at the Methane Emission Technology Evaluation Center in Colorado State University to investigate the effect of soil textural heterogeneity, soil moisture, and leak rate (0.5 and 0.85 kg/h) on methane migration caused by leaking pipelines. Subsurface methane concentrations, in addition to soil moisture and meteorological data, were collected over time. A previously validated numerical model was modified and used to understand the observed methane distribution behavior. Results of this study illustrate that the influence of soil texture, leak rate, and moisture on subsurface methane distribution is determined by the relative contribution of advection and diffusion and closely related to the distance to the leak source. Advection dominates gas transport within 1–1.5 m of the leak source, driving the migration of high concentration contours. Beyond this distance, diffusion dominates migration of lower concentration contours to the far-field. Although large leak rates initially result in faster and further gas migration, the leak rate has little influence on the diffusion dominated migration farther from the leak source. Soil moisture and texture complicate gas behavior with texture variations and elevated soil moisture conditions playing a dominant role in locally increasing methane concentrations. Scenarios highlight the importance of understanding the effects of soil moisture, texture, and leak rate on gas migration behavior in an attempt to unravel their contribution to the gas concentration within the soil environment.

Quantifying methane emissions is essential for meeting near-term climate goals and typically done using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas productions sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm with maximum values of 105.9 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios show overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher price trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deploying, and allow for timely and accurate tracking of methane emissions.