Methane Quantification Performance of the Quantitative Optical Gas Imaging (QOGI) System Using Single-Blind Controlled Release Assessment.

Quantitative optical gas imaging (QOGI) system can rapidly quantify leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system's quantification capability is needed for the successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study completed 357 individual measurements across 26 controlled releases and 71 camera positions for release rates between 0.1 kg Ch4/h and 2.9 kg Ch4/h of compressed natural gas (which accounts for more than 90% of typical component-level leaks in several production facilities). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of -67% to 200%) with individual errors between -90% and 831%, which reduced to -79% to +297% when the mean of estimates of the same controlled release from multiple camera positions was considered. Performance improved with increasing release rate, using clear sky as plume background, and at wind speeds ≤1 mph relative to other measurement conditions.

Assessing the Progress of the Performance of Continuous Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

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.

Comparison of Sub-Ppm Instrument Response Suggests Higher Detection Limits Could Be Used to Quantify Methane Emissions from Oil and Gas Infrastructure.

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.

Impact of randomly assigned "pay-as-you-go" liquefied petroleum gas prices on energy use for cooking: Experimental pilot evidence from rural Rwanda.

The disease burden related to air pollution from traditional solid-fuel cooking practices in low- and middle-income countries impacts millions of people globally. Although the use of liquefied petroleum gas (LPG) fuel for cooking can meaningfully reduce household air pollution concentrations, major barriers, including affordability and accessibility, have limited widespread adoption. Using a randomized controlled trial, our objective was to evaluate the association between the cost and use of LPG among 23 rural Rwandan households. We provided a 2-burner LPG stove with accessories and incorporated a "pay-as-you-go" (PAYG) LPG service model that included fuel delivery. PAYG services remove the large up-front cost of cylinder refills by integrating "smart meter" technology that allows participants to pay in incremental amounts, as needed. We assigned three randomized discounted prices for LPG to each household at ~4-week intervals over a 12-week period. We modeled the relationship between randomized PAYG LPG price and use (standardized to monthly periods), analyzing effect modification by relative household wealth. A 1000 Rwandan Franc (about 1 USD at the time of the study) increase in LPG price/kg was associated with a 4.1 kg/month decrease in use (95% confidence interval [CI]: -6.7, -1.6; n=69 observations). Wealth modified this association; we observed a 9.7 kg/month reduction (95% CI: -14.8, -4.5) among wealthier households and a 2.5 kg/month reduction (95% CI: -5.3, 0.3) among lower-wealth households (p-interaction=0.01). The difference in price sensitivity was driven by higher LPG use among wealthier households at more heavily discounted prices; from an 80% to 10% discount, wealthy households used 17.5 to 5.3 kg/month and less wealthy households used 6.2 to 3.1 kg/month. Our pilot-level experimental evidence of PAYG LPG in a rural low-resource setting suggests that further exploration of subsidized pricing varied by household wealth is needed to ensure future policy initiatives can achieve targets without exacerbating inequities.

Unaddressed Uncertainties When Scaling Regional Aircraft Emission Surveys to Basin Emission Estimates.

Wide-area aerial methods provide comprehensive screening of methane emissions from oil and gas (O & G) facilities in production basins. Emission detections ("plumes") from these studies are also frequently scaled to the basin level, but little is known regarding the uncertainties during scaling. This study analyzed an aircraft field study in the Denver-Julesburg basin to quantify how often plumes identified maintenance events, using a geospatial inventory of 12,629 O & G facilities. Study partners (7 midstream and production operators) provided the timing and location of 5910 maintenance events during the 6 week study period. Results indicated three substantial uncertainties with potential bias that were unaddressed in prior studies. First, plumes often detect maintenance events, which are large, short-duration, and poorly estimated by aircraft methods: 9.2 to 46% (38 to 52%) of plumes on production were likely known maintenance events. Second, plumes on midstream facilities were both infrequent and unpredictable, calling into question whether these estimates were representative of midstream emissions. Finally, 4 plumes attributed to O & G (19% of emissions detected by aircraft) were not aligned with any O & G location, indicating that the emissions had drifted downwind of some source. It is unclear how accurately aircraft methods estimate this type of plume; in this study, it had material impact on emission estimates. While aircraft surveys remain a powerful tool for identifying methane emissions on O & G facilities, this study indicates that additional data inputs, e.g., detailed GIS data, a more nuanced analysis of emission persistence and frequency, and improved sampling strategies are required to accurately scale plume estimates to basin emissions.

Point Sensor Networks Struggle to Detect and Quantify Short Controlled Releases at Oil and Gas Sites.

This study evaluated multiple commercially available continuous monitoring (CM) point sensor network (PSN) solutions under single-blind controlled release testing conducted at operational upstream and midstream oil and natural gas (O&G) sites. During releases, PSNs reported site-level emission rate estimates of 0 kg/h between 38 and 86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between the release rate and the reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of the mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of seven total PSN solutions tested during this field campaign provided site-level emission rate estimates with the site average relative error ranging from -100% to 24% for solution D, -100% to -43% for solution E, -25% for solution F (solution F was only at one site), and -99% to 430% for solution G, with an overall average of -29% across all sites and solutions. Of all the individual site-level emission rate estimates, only 11% were within ±2.5 kg/h of the study team's best estimate of site-level emissions at the time of the releases.

Methane emissions from abandoned oil and gas wells in Colorado.

Recent studies indicate emission factors used to generate bottom-up methane inventories may have considerable regional variability. The US's Environmental Protection Agency's emission factors for plugged and unplugged abandoned oil and gas wells are largely based on measurement of historic wells and estimated at 0.4 g and 31 g CH4 well-1 h-1, respectively. To investigate if these are representative of wells more recently abandoned, methane emissions were measured from 128 plugged and 206 unplugged abandoned wells in Colorado, finding the first super-emitting abandoned well (76 kg CH4 well-1 h-1) and average emissions of 0 and 586 g CH4 well-1 h-1, respectively. Combining these with other states' measurements, we update the US emission factors to 1 and 198 g CH4 well-1 h-1, respectively. Correspondingly, annual methane emissions from the 3.4 million abandoned wells in the US are estimated at between 2.6 Tg, following current methodology, and 1.1 Tg, where emissions are disaggregated for well-type. In conclusion, this study identifies a new abandoned well-type, recently-producing orphaned, that contributes 74 % to the total abandoned wells methane emissions. Including this new well-type in the bottom-up inventory suggests abandoned well emissions equate to between 22 and 49 % of total emissions from US active oil and gas production operations.

Uncertainty Quantification of Methods Used to Measure Methane Emissions of 1 g CH4 h-1.

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.

Estimating the Below-Ground Leak Rate of a Natural Gas Pipeline Using Above-Ground Downwind Measurements: The ESCAPE-1 Model.

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.

Informing Methane Emissions Inventories Using Facility Aerial Measurements at Midstream Natural Gas Facilities.

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. Therefore, there is attention on utilizing full-facility measurements using airborne, satellite, or drone (top-down (TD)) techniques to inform, improve, or validate inventories. This study utilized full-facility estimates from two independent TD methods at 15 midstream natural gas facilities in the U.S.A., which were compared with a contemporaneous daily 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 included extensions to capture sources beyond regular inventory requirements and integration of local measurements, estimated significantly lower emissions than the TD estimates for 40 of 43 paired comparisons. Significant disagreement was observed at most facilities, both between the two TD methods and between the TD estimates and operator inventory. These findings have two implications. First, improving inventory estimates will require additional on-site or ground-based diagnostic screening and measurement of all sources. Second, the TD full-facility measurement methods need to undergo further testing, characterization, and potential improvement specifically tailored for complex midstream facilities.

Performance of Continuous Emission Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

Continuous emission monitoring (CM) solutions promise to detect large fugitive methane emissions in natural gas infrastructure sooner than traditional leak surveys, and quantification by CM solutions has been proposed as the foundation of measurement-based inventories. This study performed single-blind testing at a controlled release facility (release from 0.4 to 6400 g CH4/h) replicating conditions that were challenging, but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicated a 90% probability of detection (POD) of 3-30 kg CH4/h; 6 of 11 solutions achieved a POD < 6 kg CH4/h, although uncertainty was high. Four had true positive rates > 50%. False positive rates ranged from 0 to 79%. Six solutions estimated emission rates. For a release rate of 0.1-1 kg/h, the solutions' mean relative errors ranged from -44% to +586% with single estimates between -97% and +2077%, and 4 solutions' upper uncertainty exceeding +900%. Above 1 kg/h, mean relative error was -40% to +93%, with two solutions within ±20%, and single-estimate relative errors were from -82% to +448%. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicates that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting.

Estimating Regional Methane Emission Factors from Energy and Agricultural Sector Sources Using a Portable Measurement System: Case Study of the Denver-Julesburg Basin.

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.

Investigating detection probability of mobile survey solutions for natural gas pipeline leaks under different atmospheric conditions.

The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH4 emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5-8.5 g min-1. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill-Gifford stability (PG) class A is 85% at a low survey speed (2-11 mph) and decreases to 68%, 63%, 65%, and 60% in PGSC B/C, D, E/F, and G respectively. It is generally less than 25% at a high survey speed (22-34 mph), regardless of stability conditions and leak rates. Using the measurement data, a validated DP model was further constructed and showed good performance (R2: 0.76). The options of modeled favorable weather conditions (i.e., PG stability class and wind speed) to have a high DP (e.g., >50%) are rapidly decreased with the increase in survey speed. Walking survey is applicable over a wider range of weather conditions, including PG stability class A to E/F and calm to medium winds (0-5 m s-1). A driving survey at a low speed (11 mph) can only be conducted under calm to low wind speed conditions (0-3 m s-1) to have an equivalent DP to a walking survey. Only calm wind conditions in PG A (0-1 m s-1) are appropriate for a high driving speed (34 mph). These findings showed that driving survey providers need to optimize the survey schemes to achieve a DP equivalence to the traditional walking survey.

A Methane Emission Estimation Tool (MEET) for predictions of emissions from upstream oil and gas well sites with fine scale temporal and spatial resolution: Model structure and applications.

In comparing observation based methane emission estimates for oil and gas well sites to routine emissions reported in inventories, the time scale of the measurement should match the time scale over which the inventoried emissions are estimated. Since many measurements are of relatively short duration (seconds to hours), a tool is needed to estimate emissions over these time scales rather than the annual totals reported in most emission inventories. This work presents a tool for estimating routine emissions from oil and gas well sites at multiple time scales; emissions at well sites vary over time due to changes in oil and gas production rates, operating practices and operational modes at the sites. Distributions of routine emissions (expected and inventoried) from well sites are generally skewed, and the nature and degree to which the distributions are skewed depends on the time scales over which emissions are aggregated. Abnormal emissions can create additional skew in these distributions. At very short time scales (emissions aggregated over 1 min) case study distributions presented in this work are both skewed and bimodal, with the modes depending on whether liquid storage tanks are flashing at the time of the measurement and whether abnormal emissions are occurring. At longer time scales (emissions aggregated over 1 day) distributions of routine emissions simulated in this work can have multiple modes if short duration, high emission rate events, such as liquid unloadings or large abnormal emissions, occur at the site. Multiple applications of the methane emission estimation tool (MEET), developed in this work, are presented. These results emphasize the importance of developing detailed emission inventories, which incorporate operational data, when comparing measurements to routine emissions. The model described in this work supports such comparisons and is freely available.

Modeling air emissions from complex facilities at detailed temporal and spatial resolution: The Methane Emission Estimation Tool (MEET).

Recent attention to methane emissions from oil and gas infrastructure has increased interest in comparing measurements with inventory emission estimates. While measurement methods typically estimate emissions over a few periods that are seconds to hours in length, current inventory methods typically produce long-term average emission estimates. This temporal mis-alignment complicates comparisons and leads to underestimates in the uncertainty of measurement methods. This study describes a new temporally and spatially resolved inventory emission model (MEET), and demonstrates the model by application to compressor station emissions - the key facility type in midstream natural gas operations The study looks at three common facility measurement methods: tracer flux methods for measuring station emissions, the use of ethane-methane ratios for source attribution of basin-scale estimates, and the behavior of continuous monitoring for leak detection at stations. Simulation results indicate that measurement methods likely underestimate uncertainties in emission estimates by failing to account for the variability in normal facility emissions and variations in ethane/methane ratios. A tracer-based measurement campaign could estimate emissions outside the 95% confidence interval of annual emissions 30% of the time, while ethane/methane ratios could be mis-estimated by as much as 50%. Use of MEET also highlights the need to improve data reporting from measurement campaigns to better capture the temporal and spatial variation in observed emissions.

Methane Exhaust Measurements at Gathering Compressor Stations in the United States.

Unburned methane entrained in exhaust from natural gas-fired compressor engines ("combustion slip") can account for a substantial portion of station-level methane emissions. A novel in-stack, tracer gas method was coupled with Fourier transform infrared (FTIR) species measurements to quantify combustion slip from natural gas compressor engines at 67 gathering and boosting stations owned or managed by nine "study partner" operators in 11 U.S. states. The mean methane emission rate from 63 four-stroke, lean-burn (4SLB) compressor engines was 5.62 kg/h (95% CI = 5.15-6.17 kg/h) and ranged from 0.3 to 12.6 kg/h. The mean methane emission rate from 39 four-stroke, rich-burn (4SRB) compressor engines was 0.40 kg/h (95% CI = 0.37-0.42 kg/h) and ranged from 0.01 to 4.5 kg/h. Study results for 4SLB engines were lower than both the U.S. EPA compilation of air pollutant emission factors (AP-42) and Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) by 8 and 9%, respectively. Study results for 4SRB engines were 43% of the AP-42 emission factor and 8% of the GHGI emission factor, the latter of which does not distinguish between engine types. Total annual combustion slip from the U.S. natural gas gathering and boosting sector was modeled using measured emission rates and compressor unit counts from the U.S. EPA Greenhouse Gas Reporting Program. Modeled results [328 Gg/y (95% CI = 235-436 Gg/y) of unburned methane] would account for 24% (95% CI = 17-31%) of the 1391 Gg of methane emissions for "Gathering and Boosting Stations", or 6% of the net emissions for "Natural Gas Systems" (5598 Gg) as reported in the 2020 U.S. EPA GHGI. Gathering and boosting combustion slip emissions reported in the 2020 GHGI (374 Gg) fall within the uncertainty of this model.

Estimating natural gas emissions from underground pipelines using surface concentration measurements☆.

Rapid response to underground natural gas leaks could mitigate methane emissions and reduce risks to the environment, human health and safety. Identification of large, potentially hazardous leaks could have environmental and safety benefits, including improved prioritization of response efforts and enhanced understanding of relative climate impacts of emission point sources. However, quantitative estimation of underground leakage rates remains challenging, considering the complex nature of methane transport processes. We demonstrate a novel method for estimating underground leak rates based on controlled underground natural gas release experiments at the field scale. The proposed method is based on incorporation of easily measurable field parameters into a dimensionless concentration number, ε, which considers soil and fluid characteristics. A series of field experiments was conducted to evaluate the relationship between the underground leakage rate and surface methane concentration data over varying soil and pipeline conditions. Peak surface methane concentrations increased with leakage rate, while surface concentrations consistently decreased exponentially with distance from the source. Deviations between the estimated and actual leakage rates ranged from 9% to 33%. A numerical modeling study was carried out by the TOUGH3 simulator to further evaluate how leak rate and subsurface methane transport processes affect the resulting methane surface profile. These findings show that the proposed leak rate estimation method may be useful for prioritizing leak repair, and warrant broader field-scale method validation studies. A method was developed to estimate fugitive emission rates from underground natural gas pipeline leaks. The method could be applied across a range of soil and surface covering conditions.

Detection Limits of Optical Gas Imaging for Natural Gas Leak Detection in Realistic Controlled Conditions.

Optical gas imaging (OGI) is a commonly utilized leak detection method in the upstream and midstream sectors of the U.S. natural gas industry. This study characterized the detection efficacy of OGI surveyors, using their own cameras and protocols, with controlled releases in an 8-acre outdoor facility that closely resembles upstream natural gas field operations. Professional surveyors from 16 oil and gas companies and 8 regulatory agencies participated, completing 488 tests over a 10 month period. Detection rates were significantly lower than prior studies focused on camera performance. The leak size required to achieve a 90% probability-of-detection in this study is an order-of-magnitude larger than prior studies. Study results indicate that OGI survey experience significantly impacts leak detection rate: Surveyors from operators/contractors who had surveyed more than 551 sites prior to testing detected 1.7 (1.5-1.8) times more leaks than surveyors who had completed fewer surveys. Highly experienced surveyors adjust their survey speed, examine components from multiple viewpoints, and make other adjustments that improve their leak detection rate, indicating that modifications of survey protocols and targeted training could improve leak detection rates overall.

Methane Emissions from Gathering Compressor Stations in the U.S.

Using results from a nationally representative measurement campaign at 180 gathering compressor stations conducted with nine industry partners, this study estimated emissions for the U.S. gathering sector, where sector-specific emission factors have not been previously available. The study drew from a partner station population of 1705 stations-a significantly larger pool than was available for prior studies. Data indicated that whole gas emission rates from components on gathering stations were comparable to or higher than emission factors utilized by the EPA's greenhouse gas reporting program (GHGRP) but less than emission factors used for similar components on transmission compressor stations. Field data also indicated that the national population of stations likely has a higher fraction of smaller stations, operating at lower throughput per station, than the data used to develop the per-station emission factor used in EPA's greenhouse gas inventory (GHGI). This was the first national study to incorporate extensive activity data reported to the GHGRP, including 319 basin-level reports, covering 15,895 reported compressors. Combining study emission data with 2017 GHGRP activity data, the study indicated statistically lower national emissions of 1290 [1246-1342] Gg methane per year or 66% [64-69%] of current GHGI estimates, despite estimating 17% [12-22%] more stations than the 2017 GHGI (95% confidence interval). Finally, we propose a replicable method that uses GHGRP activity data to annually update GHGI gathering and boost sector emissions.

Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region.

This study spatially and temporally aligns top-down and bottom-up methane emission estimates for a natural gas production basin, using multiscale emission measurements and detailed activity data reporting. We show that episodic venting from manual liquid unloadings, which occur at a small fraction of natural gas well pads, drives a factor-of-two temporal variation in the basin-scale emission rate of a US dry shale gas play. The midafternoon peak emission rate aligns with the sampling time of all regional aircraft emission studies, which target well-mixed boundary layer conditions present in the afternoon. A mechanistic understanding of emission estimates derived from various methods is critical for unbiased emission verification and effective greenhouse gas emission mitigation. Our results demonstrate that direct comparison of emission estimates from methods covering widely different timescales can be misleading.