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Methane is a powerful greenhouse gas with a 25 times higher 100-year warming potential than carbon dioxide and is a target for mitigation to achieve climate goals. To control and curb methane emissions, estimates are required from the sources and sectors which are typically generated using bottom-up methods. However, recent studies have shown that national and international bottom-up approaches can significantly underestimate emissions. In this study, we present three bottom-up approaches used to estimate methane emissions from all emission sectors in the Denver-Julesburg basin, CO, USA. Our data show emissions generated from all three methods are lower than historic measurements. A Tier 1/2 approach using IPCC emission factors estimated 2022 methane emissions of 358 Gg (0.8% of produced methane lost by the energy sector), while a Tier 3 EPA-based approach estimated emissions of 269 Gg (0.2%). Using emission factors informed by contemporary and region-specific measurement studies, emissions of 212 Gg (0.2%) were calculated. The largest difference in emissions estimates were a result of using the Mechanistic Air Emissions Simulator (MAES) for the production and transport of oil and gas in the DJ basin. The MAES accounts for changes to regulatory practice in the DJ basin, which include comprehensive requirements for compressors, pneumatics, equipment leaks, and fugitive emissions, which were implemented to reduce emissions starting in 2014. The measurement revealed that normalized gas loss is predicted to have been reduced by a factor of 20 when compared to 10-year-old normalization loss measurements and a factor of 10 less than a nearby oil and production area (Delaware basin, TX); however, we suggest that more measurements should be made to ensure that the long-tail emission distribution has been captured by the modeling. This study suggests that regulations implemented by the Colorado Department of Public Health and Environment could have reduced emissions by a factor of 20, but contemporary regional measurements should be made to ensure these bottom-up calculations are realistic.

Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out 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 production 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 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows 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 priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.

Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation is required. To investigate instrument response, the inlets of three trace-methane (sub-ppm) analyzers were collocated on a facility designed to release gas of known composition at known flow rates between 0.4 and 5.2 kg CH4 h−1 from simulated oil and gas infrastructure. Methane mixing ratios were measured by each instrument at 1 Hertz resolution over nine hours. While mixing ratios reported by a cavity ring-down spectrometer (CRDS)-based instrument were on average 10.0 ppm (range 1.8 to 83 ppm), a mid-infrared laser absorption spectroscopy (MIRA)-based instrument reported short-lived mixing ratios far larger than expected (range 1.8 to 779 ppm) with a similar nine-hour average to the CRDS (10.1 ppm). We suggest the peaks detected by the MIRA are likely caused by a micrometeorological phenomenon, where vortex shedding has resulted in heterogeneous methane plumes which only the MIRA can observe. Further analysis suggests an instrument like the MIRA (an optical-cavity-based instrument with cavity size ≤10 cm3 measuring at ≥2 Hz with air flow rates in the order of ≤0.3 slpm at distances of ≤20 m from the source) but with a higher detection limit (25 ppm) could detect enough of the high-concentration events to generate representative 20 min-average methane mixing ratios. Even though development of a lower-cost, high-precision, high-accuracy instrument with a 25 ppm detection threshold remains a significant problem, this has implications for the use of instrumentation with higher detection thresholds, resulting in the reduction in cost to measure methane emissions and providing a mechanism for the widespread deployment of effective leak detection and repair programs for all oil and gas infrastructure.

Methane is a greenhouse gas and identified as a key driver of near-term climate change. Bottom-up approaches estimate annual methane loss from US natural gas production and transport at 6 Tg, but recent studies suggest this may be an underestimate. To investigate this possibility, an equipment-based emissions inventory, using EPA emission factors, was developed to calculate methane emissions from oil and gas operations in the Delaware basin, USA. Emission factors and activity data were then updated using contemporary and region-specific measurement data. The original inventory estimated emissions at 315 Gg CH4 y−1 (gas production-normalized rate of 0.6% loss), while the updated inventory estimated emissions of 1500 Gg CH4 y−1 (2.8% loss). The largest changes resulted from large fugitive emissions from oil production (+430 Gg CH4 y−1), updating maintenance activity emissions (+214 Gg CH4 y−1), considering flaring inefficiency (+174 Gg CH4 y−1), and the inclusion of associated gas venting (+136 Gg CH4 y−1). This study suggests that a systematic underestimate probably exists in current bottom-up inventories and identifies sources currently missing or may be incorrect. We also strongly recommend that emission factors should be validated through direct comparison against measurement campaigns that include long-tail distributions typical of oil and gas activities.

The recent interest in measuring methane (CH4) emissions from abandoned oil and gas wells has resulted in five methods being typically used. In line with the US Federal Orphaned Wells Program’s (FOWP) guidelines and the American Carbon Registry’s (ACR) protocols, quantification methods must be able to measure minimum emissions of 1 g of CH4 h−1 to within ±20%. To investigate if the methods meet the required standard, dynamic chambers, a Hi-Flow (HF) sampler, and a Gaussian plume (GP)-based approach were all used to quantify a controlled emission (Qav; g h−1) of 1 g of CH4 h−1. After triplicate experiments, the average accuracy (Ar; %) and the upper (Uu; %) and lower (Ul; %) uncertainty bounds of all methods were calculated. Two dynamic chambers were used, one following the ACR guidelines, and a second “mobile” chamber made from lightweight materials that could be constructed around a source of emission on a well head. The average emission calculated from the measurements made using the dynamic chamber (Qav = 1.01 g CH4 h−1, Ar = +0.9%), the mobile chamber (Qav = 0.99 g CH4 h−1, Ar = −1.4%), the GP approach (Qav = 0.97 g CH4 h−1, Ar = −2.6%), and the HF sampler (Qav = 1.02 g CH4 h−1, Ar = +2.2%) were all within ±3% of 1 g of CH4 h−1 and met the requirements of the FOWP and ACR protocols. The results also suggest that the individual measurements made using the dynamic chamber can quantify emissions of 1 g of CH4 h−1 to within ±6% irrespective of the design (material, number of parts, geometrical shape, and hose length), and changes to the construction or material specifications as defined via ACR make no discernible difference to the quantification uncertainty. Our tests show that a collapsible chamber can be easily constructed around the emission source on an abandoned well and be used to quantify emissions from abandoned wells in remote areas. To our knowledge, this is the first time that methods for measuring the CH4 emissions of 1 g of CH4 h−1 have been quantitively assessed against a known reference source and against each other.

Natural gas (NG) leaks from below-ground pipelines pose safety, economic, and environmental hazards. Despite walking surveys using handheld methane (CH4) detectors to locate leaks, accurately triaging the severity of a leak remains challenging. It is currently unclear whether CH4 detectors used in walking surveys could be used to identify large leaks that require an immediate response. To explore this, we used above-ground downwind CH4 concentration measurements made during controlled emission experiments over a range of environmental conditions. These data were then used as the input to a novel modeling framework, the ESCAPE−1 model, to estimate the below-ground leak rates. Using 10-minute averaged CH4 mixing/meteorological data and filtering out wind speed < 2 m s−1/unstable atmospheric data, the ESCAPE−1 model estimates small leaks (0.2 kg CH4 h−1) and medium leaks (0.8 kg CH4 h−1) with a bias of −85%/+100% and −50%/+64%, respectively. Longer averaging (≥3 h) results in a 55% overestimation for small leaks and a 6% underestimation for medium leaks. These results suggest that as the wind speed increases or the atmosphere becomes more stable, the accuracy and precision of the leak rate calculated by the ESCAPE−1 model decrease. With an uncertainty of ±55%, our results show that CH4 mixing ratios measured using industry-standard detectors could be used to prioritize leak repairs.

Methane (CH4), a powerful greenhouse gas (GHG), has been identified as a key target for emission reduction in the Paris agreement, but it is not currently clear where efforts should be focused to make the greatest impact. Currently, activity data and standard emission factors (EF) are used to generate GHG emission inventories. Many of the EFs are globally uniform and do not account for regional variability in industrial or agricultural practices and/or regulation. Regional EFs can be derived from top–down emissions measurements and used to make bespoke regional GHG emission inventories that account for geopolitical and social variability. However, most large-scale top–down approaches campaigns require significant investment. To address this, lower-cost driving surveys (DS) have been identified as a viable alternative to more established methods. DSs can take top–down measurements of many emission sources in a relatively short period of time, albeit with a higher uncertainty. To investigate the use of a portable measurement system, a 2260 km DS was conducted throughout the Denver–Julesburg Basin (DJB). The DJB covers an area of 8000 km2 north of Denver, CO and is densely populated with CH4 emission sources, including oil and gas (O and G) operations, agricultural operations (AGOs), lakes and reservoirs. During the DS, 157 individual CH4 emission sources were detected; 51%, 43% and 4% of sources were AGOs, O and G operations, and natural sources, respectively. Methane emissions from each source were quantified using downwind concentration and meteorological data and AGOs and O and G operations represented nearly all the CH4 emissions in the DJB, accounting for 54% and 37% of the total emission, respectively. Operations with similar emission sources were grouped together and average facility emission estimates were generated. For agricultural sources, emissions from feedlot cattle, dairy cows and sheep were estimated at 5, 31 and 1 g CH4 head−1 h−1, all of which agreed with published values taken from focused measurement campaigns. Similarly, for O and G average emissions for well pads, compressor stations and gas processing plants (0.5, 14 and 110 kg CH4 facility−1 h−1) were in reasonable agreement with emission estimates from intensive measurement campaigns. A comparison of our basin wide O and G emissions to measurements taken a decade ago show a decrease of a factor of three, which can feasibly be explained by changes to O and G regulation over the past 10 years, while emissions from AGOs have remained constant over the same time period. Our data suggest that DSs could be a low-cost alternative to traditional measurement campaigns and used to screen many emission sources within a region to derive representative regionally specific and time-sensitive EFs. The key benefit of the DS is that many regions can be screened and emission reduction targets identified where regional EFs are noticeably larger than the regional, national or global averages.

A large proportion of methane emissions from natural gas production sites are released by a fraction of high-emitting sources. Here, using Monte Carlo simulations, the authors reveal that super-emitters occur due to abnormal process conditions, explaining component and site-based inventory discrepancies.

Gathering pipelines, which transport gas from well pads to downstream processing, are a sector of the natural gas supply chain for which little measured methane emissions data are available. This study performed leak detection and measurement on 96 km of gathering pipeline and the associated 56 pigging facilities and 39 block valves. The study found one underground leak accounting for 83% (4.0 kg CH4/hr) of total measured emissions. Methane emissions for the 4684 km of gathering pipeline in the study area were estimated at 402 kg CH4/hr [95 to 1065 kg CH4/hr, 95% CI], or 1% [0.2% to 2.6%] of all methane emissions measured during a prior aircraft study of the same area. Emissions estimated by this study fall within the uncertainty range of emissions estimated using emission factors from EPA’s 2015 Greenhouse Inventory and study activity estimates. While EPA’s current inventory is based upon emission factors from distribution mains measured in the 1990s, this study indicates that using emission factors from more recent distribution studies could significantly underestimate emissions from gathering pipelines. To guide broader studies of pipeline emissions, we also estimate the fraction of the pipeline length within a basin that must be measured to constrain uncertainty of pipeline emissions estimates to within 1% of total basin emissions. The study provides both substantial insight into the mix of emission sources and guidance for future gathering pipeline studies, but since measurements were made in a single basin, the results are not sufficiently representative to provide methane emission factors at the regional or national level.