Methane Leaks from Natural Gas Systems Follow Extreme Distributions

Methane emissions from residential natural gas meter set assemblies

Residential natural gas meter set assemblies (MSAs) emit methane (CH4), but reported emissions factors vary. To test existing emissions factors, we quantified CH4 emissions from 37 residential MSAs in Calgary, Alberta, Canada. A notable difference with previous studies is the targeted measurement of regulator vents in this study, which were measured with a static chamber, while fugitives were measured with a modified hi-flow sampler. Emissions were dominated by pressure regulator vents (emissions factor = 1.18 g CH4/h/MSA), but 7 fugitives were found (emissions factor = 0.018 g CH4/h/MSA). Six regulator vents were emitting at notably higher rates (≥ 1.79 g CH4/h/MSA). The total empirical emissions factor was 1.20 g CH4/h/MSA (95 % CI, 1.03 to 1.37 g/h/MSA). This is ∼7 times higher than the emissions factor for residential MSAs used in the U.S. EPA's Greenhouse Gas Inventory, which may not include emissions from regulator vents. Upscaling to annual CH4 emissions in Calgary indicates 3234.6 t CH4/yr (95 % CI, 2776.4 t to 3692.9 t CH4/yr) could be emitted from MSAs. This is equivalent to 4.1 % (95 % CI, 3.5 % to 4.7 %) of total city-level CH4 emissions as estimated with satellite data. Results suggest residential MSA emissions may be under-estimated and further study isolating root causes of regulator vent emissions is required to guide mitigation and improve emissions modeling.

Development of Company-Specific Emission Factors with Confidence Intervals for Natural Gas Customer Meters in Southern California

Methane is both a significant and short-lived greenhouse gas compared to CO2, and reducing methane emissions from natural gas distribution systems may offer cost-effective reduction opportunities. We report substantial new direct leak rate measurements from customer meter set assemblies (MSAs) in Southern California. In a novel way, emission factors are defined in terms of aboveground Hazardous and Nonhazardous leak categories, which take direct advantage of readily available industry leak data. We also studied leaks that were not detected as part of normal leak survey procedures. As a result, this yields company-specific emission factors that can be used to track progress in reducing methane emissions. This approach also has the advantage of explicitly accounting for the skewed or fat-tail distribution of leak rates by treating high flow rate MSA leaks separately from low flow rate MSA leaks. The Southern California Gas (SoCalGas) methane emission factors, based on 485 leak rate measurements by direct enclosure, were 4.55 (95% confidence interval: 2.32 to 7.14) kg/day for Hazardous leaks, 0.149 (0.119 to 0.183) kg/day for Nonhazardous leaks, and 0.0039 (0.0003 to 0.0198) kg/day for Non-Detected leaks. The percentage of surveyed meters with nondetected leaks was 29.1% (24.3 to 34.6%). Based on a robust Monte Carlo analysis, total leak emissions from MSAs for the SoCalGas system were reduced by 35% based on data from 2015 to 2022. These reductions were attributed to surveying a larger number of MSAs and accelerated leak repair rates. In traditional population-based emission inventories, an individual emission factor for a given asset category is multiplied by the total population of MSAs within the category. This approach simply cannot capture the reduction in leak numbers and methane emissions resulting from leak mitigation and prevention programs.

US oil and gas system emissions from nearly one million aerial site measurements

As airborne methane surveys of oil and gas systems continue to discover large emissions that are missing from official estimates1-4, the true scope of methane emissions from energy production has yet to be quantified. We integrate approximately one million aerial site measurements into regional emissions inventories for six regions in the USA, comprising 52% of onshore oil and 29% of gas production over 15 aerial campaigns. We construct complete emissions distributions for each, employing empirically grounded simulations to estimate small emissions. Total estimated emissions range from 0.75% (95% confidence interval (CI) 0.65%, 0.84%) of covered natural gas production in a high-productivity, gas-rich region to 9.63% (95% CI 9.04%, 10.39%) in a rapidly expanding, oil-focused region. The six-region weighted average is 2.95% (95% CI 2.79%, 3.14%), or roughly three times the national government inventory estimate5. Only 0.05-1.66% of well sites contribute the majority (50-79%) of well site emissions in 11 out of 15 surveys. Ancillary midstream facilities, including pipelines, contribute 18-57% of estimated regional emissions, similarly concentrated in a small number of point sources. Together, the emissions quantified here represent an annual loss of roughly US$1 billion in commercial gas value and a US$9.3 billion annual social cost6. Repeated, comprehensive, regional remote-sensing surveys offer a path to detect these low-frequency, high-consequence emissions for rapid mitigation, incorporation into official emissions inventories and a clear-eyed assessment of the most effective emission-finding technologies for a given region.

Ground-Based Mobile Measurements to Track Urban Methane Emissions from Natural Gas in 12 Cities across Eight Countries

To mitigate methane emission from urban natural gas distribution systems, it is crucial to understand local leak rates and occurrence rates. To explore urban methane emissions in cities outside the U.S., where significant emissions were found previously, mobile measurements were performed in 12 cities across eight countries. The surveyed cities range from medium size, like Groningen, NL, to large size, like Toronto, CA, and London, UK. Furthermore, this survey spanned across European regions from Barcelona, ES, to Bucharest, RO. The joint analysis of all data allows us to focus on general emission behavior for cities with different infrastructure and environmental conditions. We find that all cities have a spectrum of small, medium, and large methane sources in their domain. The emission rates found follow a heavy-tailed distribution, and the top 10% of emitters account for 60-80% of total emissions, which implies that strategic repair planning could help reduce emissions quickly. Furthermore, we compare our findings with inventory estimates for urban natural gas-related methane emissions from this sector in Europe. While cities with larger reported emissions were found to generally also have larger observed emissions, we find clear discrepancies between observation-based and inventory-based emission estimates for our 12 cities.

Large-Scale Controlled Experiment Demonstrates Effectiveness of Methane Leak Detection and Repair Programs at Oil and Gas Facilities

Most jurisdictions around the globe use leak detection and repair (LDAR) programs to find and fix methane leaks from oil and gas operations. In this work, we empirically evaluate the efficacy of LDAR programs using a large-scale, bottom-up, randomized controlled field experiment across ∼200 oil and gas sites in Red Deer, Canada. We find that tanks are the single largest source of emissions, contributing to nearly 60% of the total emissions. The average number of leaks at treatment sites that underwent repair reduced by ∼50% compared to the control sites. Although control sites did not see a reduction in the number of leaks, emissions reduced by approximately 36%, suggesting potential impact of routine maintenance activities to find and fix large leaks. By tracking tags on leaking equipment over time, we find a high degree of persistence; leaks that are repaired remain fixed in follow-up surveys, while non-repaired leaks remain emitting at a similar rate, suggesting that any increase in observed leak emissions following LDAR surveys are likely from new leaks. Our results show that a focus on equipment and sites that are prone to high emissions, such as tanks and oil sites, is key to cost-effective mitigation.

Methane Emissions from Non-producing Oil and Gas Wells and the Potential Role of Seismic Activity: A Case Study in Northeast British Columbia, Canada

Increasing seismic activity due to fluid injections for oil and gas production may be contributing to leakage along non-producing oil and gas wells and emitting methane, a potent greenhouse gas. However, the extent to which nearby seismicity may drive or exacerbate methane emissions and cause well integrity issues is unknown. Therefore, we analyze field evaluations at 448 non-producing oil and gas wells in Northeast British Columbia (NEBC) and geospatially analyze oil and gas well and fluid injection data alongside locations of 3515 earthquakes from 2001 to 2021 and 130 faults. Through analysis of ground and helicopter-based field evaluations of non-producing wells in NEBC, we show that methane emission rates of non-producing wells average at 8301 mg/h/well but vary by 10 orders of magnitude. We find that higher methane emission rates (milligrams of methane/h/well) are observed at wells with larger flowing pressures at the wellhead during completion (kPa) and with shorter distances (m) to earthquakes, particularly at plugged wells. These results imply that seismicity may increase the likelihood of non-producing well integrity issues and methane leakage, thereby also exacerbating groundwater contamination and environmental degradation risks.

Methane emission and influencing factors of China's oil and natural gas sector in 2020-2060: A source level analysis

The Chinese oil and gas industry requires targeted policies to reduce methane emissions. To achieve this goal, it is necessary to predict future methane emission trends and analyze the factors that influence them. However, changing economic development patterns, insufficient analysis of various factors influencing emissions, and inadequate resolution of methane emission inventories have made these goals difficult to achieve. Accordingly, this study aims to expand the methane emission estimation method to compile source-level emission inventories for future emissions, analyze the factors influencing them, and form a mechanistic understanding of the methane emissions from the local oil and gas industry. The research results indicate that methane emissions deriving from this industry will increase rapidly before 2030, after which they will decline slowly in all scenarios. The production and utilization processes in the natural gas supply chain, i.e., compressors and liquid unloading, include the main sources of methane emissions. Emissions are affected significantly by total production and consumption. Change in the overall supply and demand of natural gas affects change in methane emissions more significantly than adopting new technologies and strengthening facility maintenance, i.e., the overall supply and demand of natural gas are the dominant factors in controlling methane emissions. This study suggests that controlling the total demand for oil and gas should be at the core of the methane emission control policy for the local oil and gas industry. Moreover, equipment maintenance and emission reduction technologies should be used more effectively to reduce total emissions.

Application of Machine Learning for Calibrating Gas Sensors for Methane Emissions Monitoring

Methane leaks are a significant component of greenhouse gas emissions and a global problem for the oil and gas industry. Emissions occur from a wide variety of sites with no discernable patterns, requiring methodologies to frequently monitor these releases throughout the entire production chain. To cost-effectively monitor widely dispersed well pads, we developed a methane point instrument to be deployed at facilities and connected to a cloud-based interpretation platform that provides real-time continuous monitoring in all weather conditions. The methane sensor is calibrated with machine learning methods of Gaussian process regression and the results are compared with artificial neural networks. A machine learning approach incorporates environmental effects into the sensor response and achieves the accuracies required for methane emissions monitoring with a small number of parameters. The sensors achieve an accuracy of 1 part per million methane (ppm) and can detect leaks at rates of less than 0.6 kg/h.

Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria

Methane emissions from oil and gas production provide an important contribution to global warming. We investigate 2020 emissions from the largest gas field in Algeria, Hassi R'Mel, and the oil-production-dominated area Hassi Messaoud. We use methane data from the high-resolution (20 m) Sentinel-2 instruments to identify and estimate emission time series for 11 superemitters (including 10 unlit flares). We integrate this information in a transport model inversion that uses methane data from the coarser (7 km × 5.5 km) but higher-precision TROPOMI instrument to estimate emissions from both the 11 superemitters (>1 t/h individually) and the remaining diffuse area source (not detected as point sources with Sentinel-2). Compared to a bottom-up inventory for 2019 that is aligned with UNFCCC-reported emissions, we find that 2020 emissions in Hassi R'Mel (0.16 [0.11-0.22] Tg/yr) are lower by 53 [24-73]%, and emissions in Hassi Messaoud (0.22 [0.13-0.28] Tg/yr) are higher by 79 [4-188]%. Our analysis indicates that a larger fraction of Algeria's methane emissions (∼75%) come from oil production than national reporting suggests (5%). Although in both regions the diffuse area source constitutes the majority of emissions, relatively few satellite-detected superemitters provide a significant contribution (24 [12-40]% in Hassi R'Mel; 49 [27-71]% in Hassi Messaoud), indicating that mitigation efforts should address both. Our synergistic use of Sentinel-2 and TROPOMI can produce a unique and detailed emission characterization of oil and gas production areas.

A simple method to measure methane emissions from indoor gas leaks

From wellhead to burner tip, each component of the natural gas process chain has come under increased scrutiny for the presence and magnitude of methane leaks, because of the large global warming potential of methane. Top-down measures of methane emissions in urban areas are significantly greater than bottom-up estimates. Recent research suggests this disparity might in part be explained by gas leaks from one of the least understood parts of the process chain: behind the gas meter in homes and buildings. However, little research has been performed in this area and few methods and data sets exist to measure or estimate them. We develop and test a simple and widely deployable closed chamber method that can be used for quantifying indoor methane emissions with an order-of-magnitude precision which allows for screening of indoor large volume ("super-emitting") leaks. We also perform test applications of the method finding indoor leaks in 90% of the 20 Greater Boston buildings studied and indoor methane emissions between 0.02-0.51 ft3 CH4 day-1 (0.4-10.3 g CH4 day-1) with a mean of 0.14 ft3 CH4 day-1 (2.8 g CH4 day-1). Our method provides a relatively simple way to scale up indoor methane emissions data collection. Increased data may reduce uncertainty in bottom-up inventories, and can be used to find super-emitting indoor emissions which may better explain the disparity between top-down and bottom-up post-meter emissions estimates.

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

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 top-down 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.

Recovery and utilisation of waste heat from flue/exhaust gases: a bibliometric analysis (2010-2022)

The recovery and utilisation of waste heat from flue/exhaust gases (RU/WHFG) could potentially provide sustainable energy while curbing pollutant emissions. Over time, the RU/WHFG research landscape has gained significant traction and yielded innovative technologies, sustainable strategies, and publications. However, critical studies highlighting current advancements, publication trends, research hotspots, major stakeholders, and future research directions on RU/WHFG research remain lacking. Therefore, this paper presents a comprehensive bibliometric analysis and literature review of the RU/WHFG research landscape based on publications indexed in Scopus. Results showed that 123 publications and 2191 citations were recovered between 2010 and 2022. Publication trends revealed that the growing interest in RU/WHFG is mainly due to environmental concerns (e.g. pollution, global warming, and climate change), research collaborations, and funding availability. Stakeholder analysis revealed that numerous researchers, affiliations, and countries have actively contributed to the growth and development of RU/WHFG. Lin Fu and Tsinghua University (China) are the most prolific researchers and affiliations, whereas the National Natural Science Foundation of China (NSFC) and China are the most prolific funder and country, respectively. Funding availability from influential schemes such as NSFC has accounted for China's dominance. Keyword co-occurrence identified three major research hotspots, namely, thermal energy utilisation and management (cluster 1), integrated energy and resource recovery (cluster 2), and system analysis and optimisation (cluster 3). Literature review revealed that researchers are currently focused on maximising thermodynamic/energy efficiency, fuel minimisation, and emission reduction. Despite progress, research gaps remain in low-temperature/low-grade waste heat recovery, utilisation, storage, life cycle, and environmental impact analysis.

Minimum detection limits of the TROPOMI satellite sensor across North America and their implications for measuring oil and gas methane emissions

Methane emissions from natural gas are of ever-increasing importance as we struggle to reach Paris climate targets. Locating and measuring emissions from natural gas can be particularly difficult as they are often widely distributed across supply chains. Satellites are increasingly used to measure these emissions, with some such as TROPOMI giving daily coverage worldwide, making locating and quantifying these emissions easier. However, there is little understanding of the real-world detection limits of TROPOMI, which can cause emissions to go undetected or be misattributed. This paper uses TROPOMI and meteorological data to calculate, and create a map of, the minimum detection limits of the TROPOMI satellite sensor across North America for different campaign lengths. We then compared these to emission inventories to determine the quantity of emissions that can be captured by TROPOMI. We find that minimum detection limits vary from 500-8800 kg/h/pixel in a single overpass to 50-1200 kg/h/pixel for a yearlong campaign. This leads to 0.04 % of a year's emissions being captured in a single (day) measurement to 14.4 % in a 1-year measurement campaign. Assuming gas sites contain super-emitters, emissions of between 4.5 % - 10.1 % from a single measurement and 35.6 % - 41.1 % for a yearlong campaign are captured.

Tiered Leak Detection and Repair Programs at Simulated Oil and Gas Production Facilities: Increasing Emission Reduction by Targeting High-Emitting Sources

Distributions of methane emission rates originating from oil and gas production facilities are highly skewed and span 6-8 orders of magnitude. Traditional leak detection and repair programs have relied on surveys with handheld detectors at intervals of 2 to 4 times a year to find and fix emissions; however, this approach may lead unintended emissions to be active for the same interval independently of their magnitude. In addition, manual surveys are labor intensive. Novel methane detection technologies offer opportunities to further reduce emissions by quickly detecting the high-emitters, which account for a disproportionate fraction of total emissions. In this work, combinations of methane detection technologies with a focus of targeting high-emitting sources were simulated in a tiered approach for facilities representative of the Permian Basin, a region with skewed emission rates where emissions above 100 kg/h account for 40-80% of production site-wide total emissions, which include sensors on satellites, aircraft, continuous monitors, and optical gas imaging (OGI) cameras, with variations on survey frequency, detection thresholds, and repair times. Results show that strategies that quickly detect and fix high-emitting sources while decreasing the frequency of OGI inspections, which find the smaller emissions, achieve higher reductions than quarterly OGI and, in some cases, reduce emissions further than monthly OGI.

Comparative Assessment of Methane Emissions from Onshore LNG Facilities Measured Using Differential Absorption Lidar

This study provides results from measurements of methane emissions from three onshore LNG liquefaction facilities and two regasification facilities across different regions using the Differential Absorption Lidar (DIAL) technique. The measurement approach was to quantify, at each facility, emissions from the key functional elements (FEs), defined as spatially separable areas related to different identified processes. The DIAL technique enabled quantification of emissions at the FE level, allowing emission factors (EFs) to be determined for each FE using activity data. The comprehensive data set presented here should not be used for annualization, however shows the potential of what could be achieved with a larger sample size in terms of potential methane reduction and improving inventory accuracy. Among the benefits in obtaining data with this level of granularity is the possibility to compare the emissions of similar FEs on different plants including FEs present in both liquefaction and regasification facilities. Emissions from noncontinuous sources and superemitters can also be identified and quantified enabling more accurate inventory reporting and targeted maintenance and repair. Site throughput during the measurement periods was used to characterize total site EF; on average the methane losses were 0.018% and 0.070% of throughput at the regasification and liquefaction facilities, respectively.

Assessing Detection Efficiencies for Continuous Methane Emission Monitoring Systems at Oil and Gas Production Sites

Continuous monitoring systems, consisting of multiple fixed sensors, are increasingly being deployed at oil and gas production sites to detect methane emissions. While these monitoring systems operate continuously, their efficiency in detecting emissions will depend on meteorological conditions, sensor detection limits, the number of sensors deployed, and sensor placement strategies. This work demonstrates an approach to assess the effectiveness of continuous sensor networks in detecting infinite-duration and fixed-duration emission events. The case studies examine a single idealized source and a group of nine different sources at varying heights and locations on a single pad. Using site-specific meteorological data and dispersion modeling, the emission detection performance is characterized. For these case studies, infinite-duration emission events are detected within 1 h to multiple days, depending on the number of sensors deployed. The percentage of fixed-duration emission events that are detected ranged from less than 10% to more than 90%, depending on the number of sources, emission release height, emission event duration, and the number of sensors deployed. While these results are specific to these case studies, the analysis framework described in this work can be broadly applied in the evaluation of continuous emission monitoring network designs.

CH4, C2H6, and CO2 Multi-Gas Sensing Based on Portable Mid-Infrared Spectroscopy and PCA-BP Algorithm

A multi-gas sensing system was developed based on the detection principle of the non-dispersive infrared (NDIR) method, which used a broad-spectra light source, a tunable Fabry-Pérot (FP) filter detector, and a flexible low-loss infrared waveguide as an absorption cell. CH₄, C₂H₆, and CO₂ gases were detected by the system. The concentration of CO₂ could be detected directly, and the concentrations of CH₄ and C₂H₆ were detected using a PCA-BP neural network algorithm because of the interference of CH₄ and C₂H₆. The detection limits were achieved to be 2.59 ppm, 926 ppb, and 114 ppb for CH₄, C₂H₆, and CO₂ with an averaging time of 429 s, 462 s, and 297 s, respectively. The root mean square error of prediction (RMSEP) of CH₄ and C₂H₆ were 10.97 ppm and 2.00 ppm, respectively. The proposed system and method take full advantage of the multi-component gas measurement capability of the mid-infrared broadband source and achieve a compromise between performance and system cost.

Methane emissions from oil and gas production sites and their storage tanks in West Virginia

A measurement campaign characterized methane and other emissions from 15 natural gas production sites. Sites were surveyed using optical gas imaging (OGI) cameras to identify fugitive and vented emissions, with the methane mass emission rate quantified using a full flow sampler. We present storage tank emissions in context of all site emissions, followed by a detailed account of the former. In total, 224 well pad emission sources at 15 sites were quantified yielding a total emission rate of 57.5 ± 2.89 kg/hr for all sites. Site specific emissions ranged from 0.4 to 10.5 kg/hr with arithmetic and geometric means of 3.8 and 2.2 kg/hr, respectively. The two largest categories of emissions by mass were pneumatic devices (35 kg/hr or ~61% of total) and tanks (14.3 kg/hr or ~25% of total). Produced water and condensate tanks at all sites employed emissions control devices. Nevertheless, tanks may still lose gas via component leaks as observed in this study. The total number of tanks at all sites was 153. One site experienced a major malfunction and direct tank measurements were not conducted due to safety concerns and may have represented a super-emitter as found in other studies. The remaining sites had 143 tanks, which accounted for 42 emissions sources. Leaks on controlled tanks were associated with ERVs, PRVs, and thief hatches. Since measurements represented snapshots-in-time and could only be compared with modeled tank emission data, it was difficult to assess real capture efficiencies accurately. Our estimates suggest that capture efficiency ranged from 63 to 92% for controlled tanks.

Life Cycle GHG Perspective on U.S. Natural Gas Delivery Pathways

Recent emission measurement campaigns have improved our understanding of the total greenhouse gas (GHG) emissions across the natural gas supply chain, the individual components that contribute to these emissions, and how these emissions vary geographically. However, our current understanding of natural gas supply chain emissions does not account for the linkages between specific production basins and consumers. This work provides a detailed life cycle perspective on how GHG emissions vary according to where natural gas is produced and where it is delivered. This is accomplished by disaggregating transmission and distribution infrastructure into six regions, balancing natural gas supply and demand locations to infer the likely pathways between production and delivery, and incorporating new data on distribution meters. The average transmission distance for U.S. natural gas is 815 km but ranges from 45 to 3000 km across estimated production-to-delivery pairings. In terms of 100-year global warming potentials, the delivery of one megajoule (MJ) of natural gas to the Pacific region has the highest mean life cycle GHG emissions (13.0 g CO₂e/MJ) and the delivery of natural gas to the Northeast U.S. has the lowest mean life cycle GHG emissions (8.1 g CO₂e/MJ). The cradle-to-delivery scenarios developed in this work show that a national average does not adequately represent the upstream GHG emission intensity for natural gas from a specific basin or delivered to a specific consumer.

Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin

The rapid reduction of methane emissions, especially from oil and gas (O&G) operations, is a critical part of slowing global warming. However, few studies have attempted to specifically characterize emissions from natural gas gathering pipelines, which tend to be more difficult to monitor on the ground than other forms of O&G infrastructure. In this study, we use methane emission measurements collected from four recent aerial campaigns in the Permian Basin, the most prolific O&G basin in the United States, to estimate a methane emission factor for gathering lines. From each campaign, we calculate an emission factor between 2.7 (+1.9/-1.8, 95% confidence interval) and 10.0 (+6.4/-6.2) Mg of CH₄ year^-1 km^-1, 14-52 times higher than the U.S. Environmental Protection Agency's national estimate for gathering lines and 4-13 times higher than the highest estimate derived from a published ground-based survey of gathering lines. Using Monte Carlo techniques, we demonstrate that aerial data collection allows for a greater sample size than ground-based data collection and therefore more comprehensive identification of emission sources that comprise the heavy tail of methane emissions distributions. Our results suggest that pipeline emissions are underestimated in current inventories and highlight the importance of a large sample size when calculating basinwide pipeline emission factors.

Multiscale Methane Measurements at Oil and Gas Facilities Reveal Necessary Frameworks for Improved Emissions Accounting

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.

Monitoring methane emissions from oil and gas operations‡

The atmospheric concentration of methane has more than doubled since the start of the Industrial Revolution. Methane is the second-most-abundant greenhouse gas created by human activities and a major driver of climate change. This APS-Optica report provides a technical assessment of the current state of monitoring U.S. methane emissions from oil and gas operations, which accounts for roughly 30% of U.S. anthropogenic methane emissions. The report identifies current technological and policy gaps and makes recommendations for the federal government in three key areas: methane emissions detection, reliable and systematized data and models to support mitigation measures, and effective regulation.

A Source-Level Estimation and Uncertainty Analysis of Methane Emission in China’s Oil and Natural Gas Sector

A high-quality methane emission estimation in China’s oil and gas sector is the basis of an effective mitigation strategy. Currently, the published emission data and studies of China’s oil and gas sector only provide estimations of total emissions, which is not enough for good analysis of the trend and impact factors for the instruction of emission mitigation activities. The main problem is that published data for oil and gas infrastructure in China is incomplete, which makes it difficult to apply the conventional greenhouse gas inventory compiling method and the uncertainty estimation strategy. Therefore, this paper aims to develop a method to estimate infrastructure data using all available data, including partial data for the infrastructure, national production and consumption of oil and gas, and production and production capacity data of oil and gas enterprises, and then uses a Monte Carlo-based method to generate a source-based inventory and uncertainty analysis of methane emission for China’s oil and gas industry from 1995 to 2018. We found that methane emission increased from 208.3 kt in 1995 to 1428.8 kt in 2018. Methane emission in 2018 has an uncertainty of about ±3%. Compared to former studies, our research found that the production stage of natural gas is the main contributor, which is further driven by the growth of natural gas production. The mitigation potential introduced by technology development on methane emission remains large.

Emission Quantification via Passive Infrared Optical Gas Imaging: A Review

Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of interest, offers non-invasive remote sensing, and provides the capability for spatially resolved measurements. It has been broadly applied to emission detection, localization, and visualization; however, emission quantification is a long-standing challenge for passive IOGI. In order to facilitate the development of quantitative IOGI, in this review, we summarize theoretical findings suggesting that a single pixel value does not provide sufficient information for quantification and then we proceed to collect, organize, and summarize effective and potential methods that can support IOGI to quantify column density, concentration, and emission rate. Along the way, we highlight the potential of the strong coupling of artificial intelligence (AI) with quantitative IOGI in all aspects, which substantially enhances the feasibility, performance, and agility of quantitative IOGI, and alleviates its heavy reliance on prior context-based knowledge. Despite progress in quantitative IOGI and the shift towards low-carbon/carbon-free fuels, which reduce the complexity of quantitative IOGI application scenarios, achieving accurate, robust, convenient, and cost-effective quantitative IOGI for engineering purposes, interdisciplinary efforts are still required to bring together the evolution of imaging equipment. Advanced AI algorithms, as well as the simultaneous development of diagnostics based on relevant physics and AI algorithms for the accurate and correct extraction of quantitative information from infrared images, have thus been introduced.

Methane emissions from US low production oil and natural gas well sites

Eighty percent of US oil and natural gas (O&G) production sites are low production well sites, with average site-level production ≤15 barrels of oil equivalent per day and producing only 6% of the nation’s O&G output in 2019. Here, we integrate national site-level O&G production data and previously reported site-level CH₄ measurement data (n = 240) and find that low production well sites are a disproportionately large source of US O&G well site CH₄ emissions, emitting more than 4 (95% confidence interval: 3—6) teragrams, 50% more than the total CH₄ emissions from the Permian Basin, one of the world’s largest O&G producing regions. We estimate low production well sites represent roughly half (37—75%) of all O&G well site CH₄ emissions, and a production-normalized CH₄ loss rate of more than 10%—a factor of 6—12 times higher than the mean CH₄ loss rate of 1.5% for all O&G well sites in the US. Our work suggests that achieving significant reductions in O&G CH₄ emissions will require mitigation of emissions from low production well sites.

Only 6 percent of US oil and natural gas production output is from low production well sites. Here the authors show that total methane emissions from these low producing well sites in the US is substantial, representing about one-half of all production site methane emissions.

Methane and NOx Emissions from Natural Gas Stoves, Cooktops, and Ovens in Residential Homes

Natural gas stoves in >40 million U.S. residences release methane (CH₄)─a potent greenhouse gas─through post-meter leaks and incomplete combustion. We quantified methane released in 53 homes during all phases of stove use: steady-state-off (appliance not in use), steady-state-on (during combustion), and transitory periods of ignition and extinguishment. We estimated that natural gas stoves emit 0.8-1.3% of the gas they use as unburned methane and that total U.S. stove emissions are 28.1 [95% confidence interval: 18.5, 41.2] Gg CH₄ year^-1. More than three-quarters of methane emissions we measured originated during steady-state-off. Using a 20-year timeframe for methane, annual methane emissions from all gas stoves in U.S. homes have a climate impact comparable to the annual carbon dioxide emissions of 500 000 cars. In addition to methane emissions, co-emitted health-damaging air pollutants such as nitrogen oxides (NO_x) are released into home air and can trigger respiratory diseases. In 32 homes, we measured NO_x (NO and NO₂) emissions and found them to be linearly related to the amount of natural gas burned (r² = 0.76; p ≪ 0.01). Emissions averaged 21.7 [20.5, 22.9] ng NO_x J^-1, comprised of 7.8 [7.1, 8.4] ng NO₂ J^-1 and 14.0 [12.8, 15.1] ng NO J^-1. Our data suggest that families who don't use their range hoods or who have poor ventilation can surpass the 1-h national standard of NO₂ (100 ppb) within a few minutes of stove usage, particularly in smaller kitchens.

Implications of Generation Efficiencies and Supply Chain Leaks for the Life Cycle Greenhouse Gas Emissions of Natural Gas-Fired Electricity in the United States

Uncertainties in supply chain emissions raise questions about the benefits of natural gas as a bridge fuel, but recent efficiency improvements in gas-fired electricity generation remain overlooked. Our comprehensive analysis of supply chain infrastructure and electricity generation across the United States informs spatially and temporally resolved estimates of life cycle greenhouse gas emissions. Results show decreasing life cycle emissions over each year examined: 629, 574, and 525 kg CO₂ eq MWh^-1 in 2005, 2010, and 2015, respectively. Electricity generation contributed 86% of emissions or greater for each year. Despite concerns over uncertain methane leaks, efficiency improvements make it much more likely that natural gas electricity has an unambiguous greenhouse gas benefit relative to coal. Methane leaks would have to be 4.4 times the Environmental Protection Agency (EPA) value in 2015 to reverse these benefits over 20-year time horizons. With retiring coal plants and scrutinized supply chain emissions, our results show that natural gas can provide a lower emissions option to coal in an increasingly decarbonized power sector.

Satellites Detect Abatable Super-Emissions in One of the World's Largest Methane Hotspot Regions

Reduction of fossil fuel-related methane emissions has been identified as an essential means for climate change mitigation, but emission source identification remains elusive for most oil and gas production basins in the world. We combine three complementary satellite data sets to survey single methane emission sources on the west coast of Turkmenistan, one of the largest methane hotspots in the world. We found 29 different emitters, with emission rates >1800 kg/h, active in the 2017-2020 time period, although older satellite data show that this type of emission has been occurring for decades. We find that all sources are linked to extraction fields mainly dedicated to crude oil production, where 24 of them are inactive flares venting gas. The analysis of time series suggests a causal relationship between the decrease in flaring and the increase in venting. At the regional level, 2020 shows a substantial increase in the number of methane plume detections concerning previous years. Our results suggest that these large venting point sources represent a key mitigation opportunity as they emanate from human-controlled facilities, and that new satellite methods promise a revolution in the detection and monitoring of methane point emissions worldwide.

Climate Justice and California's Methane Superemitters: Environmental Equity Assessment of Community Proximity and Exposure Intensity

Methane superemitters emit non-methane copollutants that are harmful to human health. Yet, no prior studies have assessed disparities in exposure to methane superemitters with respect to race/ethnicity, socioeconomic status, and civic engagement. To do so, we obtained the location, category (e.g., landfill, refinery), and emission rate of California methane superemitters from Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) flights conducted between 2016 and 2018. We identified block groups within 2 km of superemitters (exposed) and 5-10 km away (unexposed) using dasymetric mapping and assigned level of exposure among block groups within 2 km (measured via number of superemitter categories and total methane emissions). Analyses included 483 superemitters. The majority were dairy/manure (n = 213) and oil/gas production sites (n = 127). Results from fully adjusted logistic mixed models indicate environmental injustice in methane superemitter locations. For example, for every 10% increase in non-Hispanic Black residents, the odds of exposure increased by 10% (95% confidence interval (CI): 1.04, 1.17). We observed similar disparities for Hispanics and Native Americans but not with indicators of socioeconomic status. Among block groups located within 2 km, increasing proportions of non-White populations and lower voter turnout were associated with higher superemitter emission intensity. Previously unrecognized racial/ethnic disparities in exposure to California methane superemitters should be considered in policies to tackle methane emissions.

Closing the methane gap in US oil and natural gas production emissions inventories

Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH4 emissions at different spatial scales are ~1.5-2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH4 mitigation policy priorities.

Decommissioning Orphaned and Abandoned Oil and Gas Wells: New Estimates and Cost Drivers

Millions of abandoned oil and gas wells are scattered across the United States, causing methane emissions and other environmental hazards. Governments are increasingly interested in decommissioning these wells but want to do so efficiently. However, information on the costs of decommissioning wells is very limited. In this analysis, we provide new cost estimates for decommissioning oil and gas wells and key cost drivers. We analyze data from up to 19,500 wells and find median decommissioning costs are roughly $20,000 for plugging only and $76,000 for plugging and surface reclamation. In rare cases, costs exceed $1 million per well. Each additional 1,000 feet of well depth increases costs by 20%, older wells are more costly than newer ones, natural gas wells are 9% more expensive than wells that produce oil, and costs vary widely by state. Surface characteristics also matter: each additional 10 feet of elevation change in the 5-acre area surrounding the well raises costs by 3%. Finally, we find that contracting in bulk pays: each additional well per contract reduces decommissioning costs by 3% per well. These findings suggest that regulators can adjust bonding requirements to better match the characteristics of each well.

Where the Methane Is-Insights from Novel Airborne LiDAR Measurements Combined with Ground Survey Data

Airborne LiDAR measurements, parallel controlled releases, and on-site optical gas imaging (OGI) survey and pneumatic device count data from 1 year prior, were combined to derive a new measurement-based methane inventory for oil and gas facilities in British Columbia, Canada. Results reveal a surprising distinction in the higher magnitudes, different types, and smaller number of sources seen by the plane versus OGI. Combined data suggest methane emissions are 1.6-2.2 times current federal inventory estimates. More importantly, analysis of high-resolution geo-located aerial imagery, facility schematics, and equipment counts allowed attribution to major source types revealing key drivers of this difference. More than half of emissions were attributed to three main sources: tanks (24%), reciprocating compressors (15%), and unlit flares (13%). These are the sources driving upstream oil and gas methane emissions, and specifically, where emerging regulations must focus to achieve meaningful reductions. Pneumatics accounted for 20%, but this contribution is lower than recent Canadian and U.S. inventory estimates, possibly reflecting a growing shift toward more low- and zero-emitting devices. The stark difference in the aerial and OGI results indicates key gaps in current inventories and suggests that policy and regulations relying on OGI surveys alone may risk missing a significant portion of emissions.

On the Long-Term Temporal Variations in Methane Emissions from an Unconventional Natural Gas Well Site

Understanding methane emissions from the natural gas supply chain continues to be of interest. Previous studies identified that measurements are skewed due to "super-emitters", and recently, researchers identified temporal variability as another contributor to discrepancies among studies. We focused on the latter by performing 17 methane audits at a single production site over 4 years, from 2016 to 2020. Source detection was similar to Method 21 but augmented with accurate methane mass rate quantification. Audit results varied from ∼78 g/h to over 43 kg/h with a mean emissions rate of 4.2 kg/h and a geometric mean of 821 g/h. Such high variability sheds light that even quarterly measurement programs will likely yield highly variable results. Total emissions were typically dominated by those from the produced water storage tank. Of 213 sources quantified, a single tank measurement represented 60% of the cumulative emission rate. Measurements were separated into four categories: wellheads (n = 78), tank (n = 17), enclosed gas process units (n = 31), and others (n = 97). Each subgroup of measurements was skewed and fat-tailed, with the skewness ranging from 2.4 to 5.7 and kurtosis values ranging from 6.5 to 33.7. Analyses found no significant correlations between methane emissions and temperature, whole gas production, or water production. Since measurement results were highly variable and daily production values were known, we completed a Monte Carlo analysis to estimate average throughput-normalized methane emissions which yielded an estimate of 0.093 ± 0.013%.

Satellite-based survey of extreme methane emissions in the Permian basin

Industrial emissions play a major role in the global methane budget. The Permian basin is thought to be responsible for almost half of the methane emissions from all U.S. oil- and gas-producing regions, but little is known about individual contributors, a prerequisite for mitigation. We use a new class of satellite measurements acquired during several days in 2019 and 2020 to perform the first regional-scale and high-resolution survey of methane sources in the Permian. We find an unexpectedly large number of extreme point sources (37 plumes with emission rates >500 kg hour^-1), which account for a range between 31 and 53% of the estimated emissions in the sampled area. Our analysis reveals that new facilities are major emitters in the area, often due to inefficient flaring operations (20% of detections). These results put current practices into question and are relevant to guide emission reduction efforts.

On the climate benefit of a coal-to-gas shift in Germany's electric power sector

Methane emissions along the natural gas supply chain are critical for the climate benefit achievable by fuel switching from coal to natural gas in the electric power sector. For Germany, one of the world’s largest primary energy consumers, with a coal and natural gas share in the power sector of 35% and 13%, respectively, we conducted fleet-conversion modelling for reference year 2018, taking domestic and export country specific greenhouse gas (GHG)-emissions in the natural gas and coal supply chains into account. Methane leakage rates below 4.9% (GWP₂₀; immediate 4.1%) in the natural gas supply chain lead to overall reduction of CO₂-equivalent GHG-emissions by fuel switching. Supply chain methane emissions vary significantly for the import countries Russia, Norway and The Netherlands, yet for Germany’s combined natural gas mix lie with << 1% far below specific break-even leakage rates. Supply chain emission scenarios demonstrate that a complete shift to natural gas would emit 30–55% (GWP₂₀ and GWP₁₀₀, respectively) less CO₂-equivalent GHG than from the coal mix. However, further abating methane emissions in the petroleum sector should remain a prime effort, when considering natural gas as bridge fuel on the path to achieve the Paris climate goals.

Repair Failures Call for New Policies to Tackle Leaky Natural Gas Distribution Systems

Methane leaks in natural gas systems are low-hanging fruit for near-term, locally driven climate policy. Recent work suggests this emissions source is larger than previously believed and that repairing a small number of high emitters can cost-effectively reduce system-wide leakage. How successful are these repairs on the ground? Here, we assess the effectiveness of repair policies in the Massachusetts distribution system. Our analysis leverages state-wide utility data, on-site empirical measurements, stakeholder interviews, and document and legal analysis. We use these mixed methods to investigate the rate of repair failure, where a gas utility identifies and fixes a leak, but on-site emissions are not eliminated. We find that repair failures are relatively common, yet they are repeatedly neglected in policy. By not accounting for repair failures, policy may overestimate the effectiveness of distribution system repairs in meeting local greenhouse gas reduction targets. These results also underscore the importance of data transparency for monitoring and verifying subnational climate policies.

Historic and Modern Approaches for the Discovery of Abandoned Wells for Methane Emissions Mitigation in Oil Creek State Park, Pennsylvania

Hundreds of oil wells were drilled along Oil Creek in Pennsylvania in the mid-1800s, birthing the modern oil industry. No longer in operation, many wells are now classified as abandoned, and, due to their age, their locations are either unknown or inaccurately recorded. These historic-well sites present environmental, safety, and economic concerns in the form of possible methane leaks and physical hazards. Airborne magnetic and LiDAR surveys were conducted in the Pioneer Run watershed in Oil Creek State Park to find abandoned wells in a historically significant but physically challenging location. Wells were drilled in this area prior to modern geolocation and legal documentation. Although a large number of old wells were abandoned summarily without remediation of the site, much of the land area within Oil Creek State Park is now covered in trees and dense underbrush, which can obscure wellheads. The thick vegetation and steep terrain limited the possibility of ground-based surveys to easily find well sites for methane emissions studies. The data from remote sensing surveys were used to corroborate potential well locations from historic maps and photographs. Potential well sites were verified in a ground-based field survey and monitored for methane emissions. Two historic photographs documenting oil activity in the late 1800s were georeferenced using a combination of magnetic and LiDAR data. LiDAR data, which were more useful in georeferencing and in field verification, identified 290 field locations in the Pioneer Run watershed, 86% of which were possible well sites. Sixty-two percent of the ground-verified wells remained unplugged and comprised the majority of leaking wells. The mean methane emissions factor for unplugged wells was 0.027 ± 0.099 kg/day, lower than other Appalachian Basin methane emissions estimates. LiDAR was used for the first time, in combination with an airborne magnetic survey, to reveal underground oil industry features and inform well identification and remediation efforts in difficult-to-navigate regions. In the oldest oil fields, where well casing has been removed or wood conductor casing was installed, historic photographs provide additional lines of evidence for oil wells where ground disturbances have concealed surface features. Identification of well sites is necessary for mitigation efforts, as unplugged wells emit methane, a potent greenhouse gas.

Historic and modern approaches for discovery of abandoned wells for methane emissions mitigation in Oil Creek State Park, Pennsylvania

BACKGROUND

Hundreds of oil wells were drilled along Oil Creek in Pennsylvania in the mid-1800s, birthing the modern oil industry. No longer in operation, many wells are now classified as abandoned, and, due to their age, their locations are either unknown or inaccurately recorded. These historic well sites present environmental, safety, and economic concerns in the form of possible methane leaks and physical hazards.

METHODS

Airborne magnetic and LiDAR surveys were conducted in the Pioneer Run watershed in Oil Creek State Park to find abandoned wells in a historically significant but physically challenging location. Wells were drilled in this area prior to modern geolocation and legal documentation. Although a large number of old wells were abandoned summarily without remediation of the site, much of the land area within Oil Creek State Park is now covered in trees and dense underbrush, which can obscure wellheads. The thick vegetation and steep terrain limited the possibility of ground-based surveys to easily find well sites for methane emissions studies. The data from remote sensing surveys were used to corroborate potential well locations from historic maps and photographs. Potential well sites were verified in a ground-based field survey and monitored for methane emissions.

RESULTS

Two historic photographs documenting oil activity in the late 1800s were georeferenced using a combination of magnetic and LiDAR data. LiDAR data, which were more useful in georeferencing and in field verification, identified 290 field locations in the Pioneer Run watershed, 86% of which were possible well sites. Sixty-two percent of the ground-verified wells remained unplugged and comprised the majority of leaking wells. The mean methane emissions factor for unplugged wells was 0.027 ± 0.099 kg/day, lower than other Appalachian Basin methane emissions estimates.

CONCLUSIONS

LiDAR was used for the first time, in combination with an airborne magnetic survey, to reveal underground oil industry features and inform well identification and remediation efforts in difficult-to-navigate regions. In the oldest oil fields, where well casing has been removed or wood conductor casing was installed, historic photographs provide additional lines of evidence for oil wells where ground disturbances have concealed surface features. Identification of well sites is necessary for mitigation efforts, as unplugged wells emit methane, a potent greenhouse gas.

Methane Emissions from Abandoned Oil and Gas Wells in Canada and the United States

Abandoned oil and gas wells are one of the most uncertain sources of methane emissions into the atmosphere. To reduce these uncertainties and improve emission estimates, we geospatially and statistically analyze 598 direct methane emission measurements from abandoned oil and gas wells and aggregate well counts from regional databases for the United States (U.S.) and Canada. We estimate the number of abandoned wells to be at least 4,000,000 wells for the U.S. and at least 370,000 for Canada. Methane emission factors range from 1.8 × 10^-3 g/h to 48 g/h per well depending on the plugging status, well type, and region, with the overall average at 6.0 g/h. We find that annual methane emissions from abandoned wells are underestimated by 150% in Canada and by 20% in the U.S. Even with the inclusion of two to three times more measurement data than used in current inventory estimates, we find that abandoned wells remain the most uncertain methane source in the U.S. and become the most uncertain source in Canada. Understanding methane emissions from abandoned oil and gas wells can provide critical insights into broader environmental impacts of abandoned wells, which are rapidly growing in number around the world.

Temporal Variability of Emissions Revealed by Continuous, Long-Term Monitoring of an Underground Natural Gas Storage Facility

Temporal variability contributes to uncertainty in inventories of methane emissions from the natural gas supply chain. Extrapolation of instantaneous, "snapshot-in-time" measurements, for example, can miss temporal intermittency and confound bottom-up/top-down comparisons. Importantly, no continuous long-term datasets record emission variability from underground natural gas storage facilities despite substantial contributions to sector-wide emissions. We present 11 months of continuous observations on a section of a storage site using dual-frequency comb spectroscopy (DCS observing system) and aircraft measurements. We find high emission variability and a skewed distribution in which the 10% highest 3 h emission periods observed by the continuous DCS observing system comprise 41% of the total observed 3-hourly emissions. Monthly emission rates differ by >12×, and 3-hourly rates vary by 17× in 24 h. We find links to the operating phase of the facility-emission rates, including as a percentage of the total gas flow rate, are significantly higher during periods of injection compared to those of withdrawal. We find that if a high frequency of aircraft flights can occur, then the ground- and aircraft-based approaches show excellent agreement in emission distributions. A better understanding of emission variability at underground natural gas storage sites will improve inventories and models of methane emissions and clarify pathways toward mitigation.

New Mexico Permian Basin Measured Well Pad Methane Emissions Are a Factor of 5-9 Times Higher Than U.S. EPA Estimates

Methane emission fluxes were estimated for 71 oil and gas well pads in the western Permian Basin (Delaware Basin), using a mobile laboratory and an inverse Gaussian dispersion method (OTM 33A). Sites with emissions that were below detection limit (BDL) for OTM 33A were recorded and included in the sample. Average emission rate per site was estimated by bootstrapping and by maximum likelihood best log-normal fit. Sites had to be split into "complex" (sites with liquid storage tanks and/or compressors) and "simple" (sites with only wellheads/pump jacks/separators) categories to achieve acceptable log-normal fits. For complex sites, the log-normal fit depends heavily on the number of BDL sites included. As more BDL sites are included, the log-normal distribution fit to the data is falsely widened, overestimating the mean, highlighting the importance of correctly characterizing low end emissions when using log-normal fits. Basin-wide methane emission rates were estimated for the production sector of the New Mexico portion of the Permian and range from ∼520 000 tons per year, TPY (bootstrapping, 95% CI: 300 000-790 000) to ∼610 000 TPY (log-normal fit method, 95% CI: 330 000-1 000 000). These estimates are a factor of 5.5-9.0 times greater than EPA National Emission Inventory (NEI) estimates for the region.

A NDIR Mid-Infrared Methane Sensor with a Compact Pentahedron Gas-Cell

In order to improve the performance of the large divergence angle mid-infrared source in gas sensing, this paper aims at developing a methane (CH₄) sensor with non-dispersive infrared (NDIR) technology using a compact pentahedron gas-cell. A paraboloid concentrator, two biconvex lenses and five planar mirrors were used to set up the pentahedron structure. The gas cell is endowed with a 170 mm optical path length with a volume of 19.8 mL. The mathematical model of the cross-section and the three-dimension spiral structure of the pentahedron gas-cell were established. The gas-cell was integrated with a mid-infrared light source and a detector as the optical part of the sensor. Concerning the electrical part, a STM32F429 was employed as a microcontroller to generate the driving signal for the IR source, and the signal from the detector was sampled by an analog-to-digital converter. A static volumetric method was employed for the experimental setup, and 20 different concentration CH₄ samples were prepared to study the sensor’s evaluation, which revealed a 1σ detection limit of 2.96 parts-per-million (ppm) with a 43 s averaging time.

A National Estimate of Methane Leakage from Pipeline Mains in Natural Gas Local Distribution Systems

We estimate methane emissions from U.S. local distribution natural gas (NG) pipes using data collected from an advanced mobile leak detection (AMLD) platform. We estimate that there are 630,000 leaks in U.S. distribution mains, resulting in methane emissions of 0.69 Tg/year (95% cr int: 0.25, 1.23). Total emissions are calculated as the product of activity factors and emissions factors. Our analysis leveraged data on >4000 leak indications found using AMLD, combined with utility pipeline GIS information, to allow us to estimate activity factors. We derive emissions factors from AMLD emission rate estimates and correct these emissions factors based on data from in-field studies assessing AMLD emissions estimates. Finally, we quantify uncertainty in both emissions factors and activity factors and propagate the uncertainty to our total emissions estimate. In modeling leak frequency, we find a clear interaction between pipeline material and age with the leakiness of all material types increasing with age. Our national methane emissions estimate is approximately 5× greater (95% cr int: 1.7×, 8.7×) than the U.S. Environmental Protection Agency's current greenhouse gas inventory estimate for pipeline mains in local distribution systems due to both a larger estimated number of leaks and better characterization of the upper tail of the skewed distribution of emission rates.

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.

Reported Methane Emissions from Active Oil and Gas Wells in Pennsylvania, 2014-2018

Oil/gas well integrity failures are a common but poorly constrained source of methane emissions to the atmosphere. As of 2014, Pennsylvania requires gas and oil well operators to report gas losses, both fugitive and process, from all active and unplugged abandoned gas and oil wells. We analyze 589,175 operator reports and find that lower-bound reported annual methane emissions averaged 22.1 Gg (-16.9, +19.5) between 2014 and 2018 from 62,483 wells, an average of only 47% of the statewide well inventory for those years. Extrapolating to the 2019 oil and gas well inventory yields well average emissions of 55.6 Gg CH₄. These emissions are not currently included in the state's oil and gas emission inventory. We also assess compliance in reporting among operators and note anomalies in reporting and apparent workarounds to reduce reported emissions. Suggestions for improving the accuracy and reliability in reporting and reducing emissions are offered.

Off-Site Flux Estimates of Volatile Organic Compounds from Oil and Gas Production Facilities Using Fast-Response Instrumentation

Flux estimates of volatile organic compounds (VOCs) from oil and gas (O&G) production facilities are fundamental in understanding hazardous air pollutant concentrations and ozone formation. Previous off-site emission estimates derive fluxes by ratioing VOCs measured in canisters to methane fluxes measured in the field. This study uses the Environmental Protection Agency's Other Test Method 33A (OTM 33A) and a fast-response proton transfer reaction mass spectrometer to make direct measurements of VOC emissions from O&G facilities in the Upper Green River Basin, Wyoming. We report the first off-site direct flux estimates of benzene, toluene, ethylbenzene, and xylenes from upstream O&G production facilities and find that these estimates can vary significantly from flux estimates derived using both the canister ratio technique and from the emission inventory. The 32 OTM 33A flux estimates had arithmetic mean (and 95% CI) as follows: benzene 17.83 (0.22, 98.05) g/h, toluene 34.43 (1.01, 126.76) g/h, C8 aromatics 37.38 (1.06, 225.34) g/h, and methane 2.3 (1.7, 3.1) kg/h. A total of 20% of facilities measured accounted for ∼67% of total BTEX emissions. While this heavy tail is less dramatic than previous observations of methane in other basins, it is more prominent than that predicted by the emission inventory.

Beyond-the-Meter: Unaccounted Sources of Methane Emissions in the Natural Gas Distribution Sector

The United States Environmental Protection Agency maintains an inventory of greenhouse gas emissions in accordance with the Intergovernmental Panel on Climate Change. Methane (CH₄), a potent gas with a global warming potential 86-125× that of carbon dioxide (CO₂) over a twenty-year period, is the main component of natural gas (NG). As NG becomes an increasingly larger percentage of the energy resources used in the United States, it is ever more important to evaluate the CH₄ emissions inventory. However, the inventory also does not account for all possible sources of CH₄ leaks, contributing to uncertainty in the national CH₄ inventory. Discrepancies between top-down and bottom-up inventories of CH₄ emissions imply that there are significant unaccounted-for sources of CH₄ leaks, especially over cities. Diffuse CH₄ plumes above cities that are not attributable to distribution pipelines or other NG infrastructure suggest many small beyond-the-meter leaks together contribute to large emissions. Here, we evaluate the distribution sector of the CH₄ emissions inventory and make suggestions to improve the inventory by analyzing end-user emissions. Preliminary research into beyond-the-meter emissions suggests that while individually small, the appliances and buildings that make up the residential sector could contribute significantly to national scale emissions. Furnaces are the most leak-prone of appliances, contributing to 0.14% of total CH₄ emissions from the NG sector in the United States. Combining measurements from whole house emissions and steady-state operation of appliances, we estimate that residential homes and appliances could release 9.1 Gg CH₄ yearly in the United States, totaling over 2% of the CH₄ released from the NG sector. While factors such as appliance age and usage, climate, and residential setting could influence the emissions profile of individual appliances, these preliminary estimates justify further exploration of beyond-the-meter emissions.

Seasonal and diurnal variations of greenhouse gases in Florence (Italy): Inferring sources and sinks from carbon isotopic ratios

In this study, the results of a continuous monitoring of (i) CO₂ fluxes, and (ii) CO₂ and CH₄ concentrations and carbon isotopic ratios (δ¹³C-CO₂ and δ¹³C-CH₄) in air, carried out from 7 to 21 July 2017 and from October 10 to December 15, 2017 in the city centre of Florence, are presented. The measurements were performed from the roof of the historical building of the Ximenes Observatory. CO₂ flux data revealed that the metropolitan area acted as a net source of CO₂ during the whole observation period. According to the Keeling plot analysis, anthropogenic contributions to atmospheric CO₂ were mainly represented by vehicular traffic (about 30%) and natural gas combustion (about 70%), the latter contributing 7 times more in December than in July. Moreover, the measured CO₂ fluxes were about 80% higher in fall than in summer, confirming that domestic heating based on natural gas is the dominant CO₂ emitting source in the municipality of Florence. Even though the continuous monitoring revealed a shift in the δ¹³C-CO₂ values related to photosynthetic uptake of atmospheric CO₂, the isotopic effect induced by plant activity was restricted to few hours in October and, to a lesser extent, in November. This suggests that urban planning policies should be devoted to massively increase green infrastructures in the metropolitan area in order to counterbalance anthropogenic emissions. During fall, the atmospheric CH₄ concentrations were sensibly higher with respect to those recorded in summer, whilst the δ¹³C-CH₄ values shifted towards heavier values. The Keeling plot analysis suggested that urban CH₄ emissions were largely related to fugitive emissions from the natural gas distribution pipeline network. On the other hand, δ¹³C-CH₄ monitoring allowed to recognize vehicular traffic as a minor CH₄ emitting source.

A Common Risk Classification Concept for Safety Related Gas Leaks and Fugitive Emissions?

Gas leaks in the oil and gas industry represent a safety risk as they, if ignited, may result in severe fires and/or explosions. Unignited, they have environmental impacts. This is particularly the case for methane leaks due to a significant Global Warming Potential (GWP). Since gas leak rates may span several orders of magnitude, that is, from leaks associated with potential major accidents to fugitive emissions on the order of 10−6 kg/s, it has been difficult to organize the leaks in an all-inclusive leak categorization model. The motivation for the present study was to develop a simple logarithmic table based on an existing consequence matrix for safety related incidents extended to include non-safety related fugitive emissions. An evaluation sheet was also developed as a guide for immediate risk evaluations when new leaks are identified. The leak rate table and evaluation guide were tested in the field at five land-based oil and gas facilities during Optical Gas Inspection (OGI) campaigns. It is demonstrated how the suggested concept can be used for presenting and analysing detected leaks to assist in Leak Detection and Repair (LDAR) programs. The novel categorization table was proven valuable in prioritizing repair of “super-emitter” components rather than the numerous minor fugitive emissions detected by OGI cameras, which contribute little to the accumulated emissions. The study was limited to five land based oil and gas facilities in Norway. However, as the results regarding leak rate distribution and “super-emitter” contributions mirror studies from other regions, the methodology should be generally applicable. To emphasize environmental impact, it is suggested to include leaking gas GWP in future research on the categorization model, that is, not base prioritization solely on leak rates. Research on OGI campaign frequency is recommended since frequent coarse campaigns may give an improved cost benefit ratio.

Environmental Assessment of Energy Scenarios for a Low-Carbon Electrical Network in Chile

Nowadays, establishing clean energy sources is an undeniable need for all territories to reconcile energy and competitiveness objectives with those of security and sustainability. This article shows the main advantages of implementing clean energy sources in the long-term Chilean electrical network. The clean energy considered in this work is based on Renewable Energy (Conventional and Non-Conventional) with the backup of gas or nuclear. Thus, four scenarios are proposed and were simulated for the year 2050, the year assumed for the decommissioning of all coal plants in the country. These scenarios contemplate a high or low penetration of Renewable Energy. Additionally, a reference and realistic scenario for the year 2018 has also been considered to compare to the clean scenarios proposed. The results obtained coincide with the goals of reducing environmental impacts such as global warming emissions and fossil fuel dependence. However, the backup that was chosen for supporting the intermittence of renewable energy may have an important role in the main system considering the expected growth of energy demands in the near future.