Assessment of methane emissions from oil and gas production pads using mobile measurements

Characterizing multifaceted environmental risks of oil and gas well leakage through soil and well methane and hydrogen sulfide emissions

Oil and gas wells (OGWs) can lead to soil and well emissions of methane (CH4), a potent greenhouse gas, and hydrogen sulfide (H2S), a highly toxic gas, both of which reduce air quality and can cause explosions when emitted into confined spaces. Developments have been occurring over OGWs, posing health and safety risks. However, to our knowledge, previous studies have not conjunctively analyzed well and soil emissions while considering development on or near OGWs. In this paper, we characterize 343 CH4 and H2S emission rate measurements from 67 non-producing (abandoned) and 35 producing (active) OGWs, including 205 measurements from soils surrounding 81 OGWs in Ontario and Quebec. We also provide the first emission rate estimates from an abandoned water and OGW-linked explosion and map OGWs in urban and built-up areas in Ontario and Quebec. We estimate the explosion-linked emissions to be 3,000 g CH4/hour and 7 g H2S/hour. Moreover, we find that 7,264 and 161 OGWs in Ontario and Quebec, respectively, are in urban and built-up areas, with 94% of these wells being abandoned. For the 102 wells we measured, of which 9.7% had H2S detections, we find OGW emission rate ranges of -16 to 47,000 mg CH4/hour and 0.001 to 3,300 mg H2S/hour. Although soil CH4 emissions at a 1-m distance from the wells are most correlated with well emissions, the highest soil emission rate was observed at a 3-m distance, indicating the potential for OGW-related emissions into buildings to occur away from the well. Overall, our multi-faceted measurement dataset provides a basis for conjunctive analysis of the broad range of environmental risks of OGWs to climate, indoor and outdoor air quality, and explosions.

Methane emissions at pressure-regulating stations in China: A comparative analysis of various quantitative methods

As a key component of the distribution system, metering and pressure-regulating (M&R) stations provide an opportunity for effective mitigation of methane emissions. Given that these stations are readily accessible, above-ground facilities, routine methane emission monitoring can identify issues and with repair or upgrades this can lead to reduced methane emissions. This practice has become an important measure for addressing climate change. China aims to actively promote the monitoring, evaluation, and inventory improvement of distribution systems. In this study, methane detection and analysis were conducted at 11 pressure regulation-metering stations operated by a company in North China and Central China. Methane emissions were estimated using the dynamic flux chamber method, the inverse Gaussian plume modeling method (OTM33A), and the stochastic Lagrangian (LS) inverse modeling method. The results of the dynamic flux chamber method indicate that flanges, instrumentation and meters, and connectors are the primary sources of methane emissions at pressure-regulating stations, with emission factors of 0.0034 kg/h (CI: 0.0030-0.0038 kg/h), 0.0022 kg/h (CI: 0.0018-0.0025 kg/h), and 7.1e-04 (CI: 6.3e-04-8.0e-04) kg/h, respectively. The correlation between detected concentration and emission rate was weak. The cumulative fluxes calculated by the three methods were 0.109 kg/h (CI: 0.094-0.125 kg/h), 0.143 kg/h (CI: 0.015-0.337 kg/h), and 0.125 kg/h (CI: 0.035-0.218 kg/h). The results of controlled tests and field measurements suggest that the LS method offers a more reliable and physically accurate analysis of methane fluxes for pressure regulating-metering stations with limited measurement distances (typically less than 30 m) and small continuous emissions. The emission factor for the pressure-regulating stations in this study is 0.013 kg/h (CI: 0.008-0.023 kg/h) using the bootstrap Monte Carlo method. This research provides guidance for operators to implement fixed monitoring, which is crucial for building emission inventories and ensuring production safety.

Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data

Air sensors can provide valuable non-regulatory and supplemental data as they can be affordably deployed in large numbers and stationed in remote areas far away from regulatory air monitoring stations. Air sensors have inherent limitations that are critical to understand before collecting and interpreting the data. Many of these limitations are mechanistic in nature, which will require technological advances. However, there are documented quality assurance (QA) methods to promote data quality. These include laboratory and field evaluation to quantitatively assess performance, the application of corrections to improve precision and accuracy, and active management of the condition or state of health of deployed air quality sensors. This paper summarizes perspectives presented at the U.S. Environmental Protection Agency's 2023 Air Sensors Quality Assurance Workshop (https://www.epa.gov/air-sensor-toolbox/quality-assurance-air-sensors#QAworkshop) by stakeholders (e.g., manufacturers, researchers, air agencies) and identifies the most pressing needs. These include QA protocols, streamlined data processing, improved total volatile organic compound (TVOC) data interpretation, development of speciated VOC sensors, and increased documentation of hardware and data handling. Community members using air sensors need training and resources, timely data, accessible QA approaches, and shared responsibility with other stakeholders. In addition to identifying the vital next steps, this work provides a set of common QA and QC actions aimed at improving and homogenizing air sensor QA that will allow stakeholders with varying fields and levels of expertise to effectively leverage air sensor data to protect human health.

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

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.

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 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.

Reconciling a national methane emission inventory with in-situ measurements

Reconciling top-down and bottom-up country-level greenhouse gas emission estimates remains a key challenge in the MRV (Monitoring, Reporting, Verification) paradigm. Here we propose to independently quantify cumulative emissions from a significant number of methane (CH4) emitters at national level and derive robust constraints for the national inventory. Methane emissions in Cyprus, an insular country, stem primarily from waste and agricultural activities. We performed 24 intensive survey days of mobile measurements of CH4 from October 2020 to September 2021 at emission 'hotspots' in Cyprus accounting together for about 28 % of national CH4 emissions. The surveyed areas include a large active landfill (Koshi, 8 % of total emissions), a large closed landfill (Kotsiatis, 18 %), and a concentrated cattle farm area (Aradippou, 2 %). Emission rates for each site were estimated using repeated downwind transects and a Gaussian plume dispersion model. The calculated methane emissions from landfills of Koshi and Kotsiatis (25.9 ± 6.4 Gg yr-1) and enteric fermentation of cattle (10.4 ± 4.4 Gg yr-1) were about 129 % and 40 % larger, respectively than the bottom-up sectorial annual estimates used in the national UNFCCC inventory. The parametrization of the Gaussian plume model dominates the uncertainty in our method, with a typical 21 % uncertainty. Seasonal variations have little influence on the results. We show that using an ensemble of in situ measurements targeting representative methane emission hotspots with consistent temporal and spatial coverage can contribute to the monitoring and validation of national bottom-up emission inventories.

Estimating inputs for dispersion modeling in mobile platform applications

Mobile monitoring platforms (MMP) are popular in air quality studies. One application of MMP is in estimating pollutant emissions from area sources. The MMP is used to measure concentrations of the relevant species at several locations around the area source, while the associated meteorological information is measured at the same time. Emissions from the area source are inferred by fitting the measured concentrations to estimates from dispersion models. These models require meteorological inputs, such as the kinematic heat flux and the surface friction velocity, that are best computed with measurements of time resolved velocity and temperature made with 3-D sonic anemometers. Because the setting up and dismantling of a 3-D sonic anemometer is not compatible with the necessary mobility of the MMP, it is useful to use alternative instrumentation and methods that provide accurate estimates of these inputs. In this study, we demonstrate such a method based on measurements of horizontal wind speed and temperature fluctuations at a single height. The method was evaluated by comparing methane emissions from a dairy manure lagoon inferred from a dispersion model that uses modeled meteorological inputs to those inferred from measurements with 3-D sonic anemometers. The emission estimates from the modeled meteorological inputs were close to those based on measurements made with 3-D sonic anemometers. We then demonstrate how this approach can be adapted for mobile platform applications by showing that winds measured using a 2-D sonic anemometer and temperature fluctuations measured with a bead thermistor, which can all be carried or mounted on a MMP, yields results that are close to those from a 3-D sonic anemometer.

Assessment of Chemical Facility Ethylene Oxide Emissions Using Mobile and Multipoint Monitoring

Ethylene oxide (EtO) is a hazardous air pollutant that can be emitted from a variety of difficult to measure industrial sources, such as fugitive leaks, wastewater handling, and episodic releases. Emerging next generation emission measurement (NGEM) approaches capable of time-resolved, low parts per billion by volume (ppbv) method detection limits (MDLs) can help facilities understand and reduce EtO and other air pollutant emissions from these sources yielding a range of environmental and public health benefits. In October 2021, a first of its kind 4-day observational study was conducted at an EtO chemical facility in the midwestern United States. The study had dual objectives to both improve understanding of EtO emission sources within the facility and advance NGEM methods. Using cavity ring-down spectroscopy (CRDS) instruments, a combination of mobile surveys and stationary multipoint process unit monitoring assessed EtO concentrations in and near facility operations, while testing and comparing measurement methods. The study concluded that four main areas of EtO source emissions existed within the facility, each possessing unique emission characteristics. Episodic EtO emissions from supply railcar switchovers and batch reactor washouts, lasting seconds to minutes in duration, produced EtO concentrations exceeding 500 ppbv inside the process unit in some cases. In one instance, EtO at ~30 ppbv was briefly observed hundreds of meters from the process unit. Lower level but more sustained EtO concentrations were observed near an EtO transfer pump and wastewater tank outfall and drain system. Overall, 4.6% of mobile survey data were above the 1.2 ppbv mobile test MDL while the nine stationary sampling locations ranged from 17.7% to 82.8% of data above the 1.0 ppbv multipoint test MDL. This paper describes the EtO emissions observed in and near the four defined source areas within the facility and provides details of the NGEM method development advances accomplished as part of the study.

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.

Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Researchers are searching for ways to better quantify methane emissions from natural gas infrastructure. Current indirect quantification techniques (IQTs) allow for more frequent or continuous measurements with fewer personnel resources than direct methods but lack accuracy and repeatability. Two IQTs are Other Test Method (OTM) 33A and Eddy Covariance (EC). We examined a novel approach to improve the accuracy of single sensor IQT whereby the results from both OTM and EC were combined with two machine learning (ML) models, a random forest (RF) and a neural network (NN). Then, models were enhanced with feature reduction and hyper-parameter tuning and compared to traditional quantification methods. The NN and RF improved upon the default OTM by an average of 44% and 78%, respectively. When compared to traditional OTM estimates with low Data Quality Indicators (DQIs), RF and NN models reduced 1σ errors from ±66% to ±13% and ±34%, respectively. Models also reduced the standard deviation of estimates with 93% and 85% of estimates falling within ±50% of the known release rate. This approach can be deployed with single sensor systems at well sites to improve confidence in reported emissions, reducing the number of anomalous overestimates that would trigger unnecessary site evaluations. Additional improvements could be realized by expanding training datasets with more methane release rates. Further, deployment of such models in a variety of situations could enhance their ability help close the gap between bottom-up inventory and top-down studies by enabling continuous monitoring of temporal emissions that could identify with improved confidence, atypically higher emissions. Accurate remote single sensor systems are key in developing an improved understanding of methane emissions to enable industry to identify and reduce methane emissions.

Temporal variation and grade categorization of methane emission from LNG fueling stations

Natural gas is increasingly seen as the fossil fuel of choice for China as it transitions to renewable sources. The significant development of China's LNG vehicle application and fueling stations and the urgency of climate changes make it particularly important to quantify methane emission from LNG stations, where the data are extremely rare. We carried out a pilot study on direct measurement and quantitative analysis of methane emission from five LNG fueling stations located in Shandong, China following the standard stationary EPA OTM 33A method. The measured methane emission of these five stations vary from 0.01 to 8.76 kg/h. The loss rates vary from 0.004 to 0.257%. We demonstrated that the emission from LNG stations consist of continuous and intermittent contents. The intermittent emission shows a strong temporal variation. If a station is only monitored for 20 min, it may either under-estimate or over-estimate the total emission. Both the distribution of emission events and total emission rates among different stations are highly skewed. We found that these LNG fueling station emission can be categorized into 3 grades, as low, medium and high, corresponding to emission rates below 0.1 kg/h; between 0.1 and 1 kg/h and above 1 kg/h, which can be characterized by the measured average methane concentration enhancement.

Development and validation of a route planning methodology for vehicle-based remote measurements of methane and other emissions from oil and gas wells and facilities

Multi-sensor vehicle systems have been implemented in large-scale field programs to detect, attribute, and estimate emissions rates of methane (CH₄) and other compounds from oil and gas wells and facilities. Most vehicle systems use passive sensing; they must be positioned downwind of sources to detect emissions. A major deployment challenge is predicting the best measurement locations and driving routes to sample infrastructure. Here, we present and validate a methodology that incorporates high-resolution weather forecast and geospatial data to predict measurement locations and optimize driving routes. The methodology estimates the downwind road intersection point (DRIP) of theoretical CH₄ plumes emitted from each well or facility. DRIPs serve as waypoints for Dijkstra's shortest path algorithm to determine the optimal driving route. We present a case study to demonstrate the methodology for planning and executing a vehicle-based concentration mapping survey of 50 oil and gas wells near Pecos, Texas. Validation was performed by comparing DRIPs with 174 CH₄ plumes measured by vehicle surveys of oil and gas wells and facilities in Alberta, Canada. Results indicate median Manhattan distances of 145.8 m between DRIPs and plume midpoints and 160.3 m between DRIPs and peak plume enhancements. A total of 46 (26%) of the plume segments overlapped DRIPs. Locational errors of DRIPs are related to misattributions of emissions sources and discrepancies between modeled and instantaneous wind direction measured when the vehicle intersects plumes. Although the development of the methodology was motivated by CH₄ emissions from oil and gas facilities, it should be applicable to other types of point source air emissions from known facilities.Implications: This paper presents and validates a method that addresses the challenge of measuring industrial emissions from public roads. The method can increase the effectiveness and efficiency of targeted vehicle-based emissions surveys where the locations of potential sources are known. We believe the method has broad application in addition to the upstream oil and gas context it was designed for.

Measurement of methane emissions from CNG fueling stations in East China

Methane, as the second most emitted greenhouse gas (GHG), has a warming potential of approximately 86 times that of carbon dioxide within 20 years. Quantifying methane emissions is helpful to the country's emission reduction efforts. However, currently, there is a lack of measurement data of methane emissions from natural gas fueling stations in China. In this study, a downwind quantification approach was employed to directly measure the methane emissions of nine compressed natural gas (CNG) fueling stations in East China according to the Environmental Protection Agency's Other Test Method 33A (OTM 33A). Moreover, methane concentrations were also measured near the nozzle of the refueling dispenser and the process equipment in the station. The methane emissions of the nine stations lied within the range of 0.11-0.83 kg/h, and the distribution of the emission rate was skewed. It was found that the emissions from gas fueling stations could be divided into intermittent emissions and continuous emissions, of which the intermittent emissions were the main source of methane.

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.

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.

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.

Understanding the Accuracy Limitations of Quantifying Methane Emissions Using Other Test Method 33A

Researchers have utilized Other Test Method (OTM) 33A to quantify methane emissions from natural gas infrastructure. Historically, errors have been reported based on a population of measurements compared to known controlled releases of methane. These errors have been reported as 2σ errors of ±70%. However, little research has been performed on the minimum attainable uncertainty of any one measurement. We present two methods of uncertainty estimation. The first was the measurement uncertainty of the state-of-the-art equipment, which was determined to be ±3.8% of the estimate. This was determined from bootstrapped measurements compared to controlled releases. The second approach of uncertainty estimation was a modified Hollinger and Richardson (H&R) method which was developed for quantifying the uncertainty of eddy covariance measurements. Using a modified version of this method applied to OTM 33A measurements, it was determined that uncertainty of any given measurement was ±17%. Combining measurement uncertainty with that of stochasticity produced a total minimum uncertainty of 17.4%. Due to the current nature of stationary single-sensor measurements and the stochasticity of atmospheric data, such uncertainties will always be present. This is critical in understanding the transport of methane emissions and indirect measurements obtained from the natural gas industry.

Declining methane emissions and steady, high leakage rates observed over multiple years in a western US oil/gas production basin

Methane, a potent greenhouse gas, is the main component of natural gas. Previous research has identified considerable methane emissions associated with oil and gas production, but estimates of emission trends have been inconsistent, in part due to limited in-situ methane observations spanning multiple years in oil/gas production regions. Here we present a unique analysis of one of the longest-running datasets of in-situ methane observations from an oil/gas production region in Utah’s Uinta Basin. The observations indicate Uinta methane emissions approximately halved between 2015 and 2020, along with declining gas production. As a percentage of gas production, however, emissions remained steady over the same years, at ~ 6–8%, among the highest in the U.S. Addressing methane leaks and recovering more of the economically valuable natural gas is critical, as the U.S. seeks to address climate change through aggressive greenhouse emission reductions.

Methane emissions from above-ground natural gas distribution facilities in the urban environment: A fence line methodology and case study in Calgary, Alberta, Canada

The occurrence and emissions of methane (CH₄) from above-ground urban natural gas infrastructure is poorly understood. Compared to below-ground infrastructure, these facilities are relatively easy to monitor and maintain and present an opportunity for cost-effective CH₄ reductions. We present a case study and methodology for detecting, attributing, and quantifying CH₄ emissions from fence line measurements at above-ground natural gas facilities in the City of Calgary, Alberta, Canada. We produced bounding-box concentration maps by walking around the outer fence of 33 facilities with a backpack-configured trace gas analyzer and a tablet with integrated GPS. Wind measurements were acquired simultaneously from a fixed location on site with a 3D sonic anemometer. We fused geolocation, CH₄ concentration, and wind data to determine the likelihood each facility was emitting. We found one definitive leak by carrying out measurements directly alongside an exposed section of pipe. Based on the presence of methyl mercaptan (CH₃SH) odor, peak ΔCH₄, and the difference between downwind and upwind ΔCH₄, we interpret a high plausibility that 22 facilities were emitting CH₄, followed by 2 with a medium plausibility, and 8 with a low plausibility. Once verified to plausibly emit, these data were used to estimate emissions flux at six facilities where near-field obstructions were limited. The estimated emissions flux for six facilities was 66.31 mg CH₄ s^-1, or 2.1 tonnes CH₄ yr^-1 if this flux remained constant. Overall, this study indicates most above-ground natural gas facilities surveyed in Calgary were emitting CH₄. These facilities represent easy mitigation targets for reducing CH₄ emissions and improving environmental performance. To our knowledge, this is the first study to integrate qualitative and quantitative information to predict detection plausibility in a complex measurement setting.Implications: The fence line methodology outlined in this study represents an extension of source assessment modes in the US EPA's Other Test Method 33A for human portable systems. This has implications for standardization of emissions measurement in situations where other platforms (e.g., vehicles) are less effective due to access limitations. We believe the methodology presented could become a recognized standard based on performance from controlled testing and added to the regulatory toolkit for emissions verification and compliance.

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

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.

Greenhouse Gas Emissions of Western Canadian Natural Gas: Proposed Emissions Tracking for Life Cycle Modeling

Natural gas (NG) produced in Western Canada is a major and growing source of Canada's energy and greenhouse gas (GHG) emissions portfolio. Despite recent progress, there is still only limited understanding of the sources and drivers of Western Canadian greenhouse gas (GHG) emissions. We conduct a case study of a production facility based on Seven Generation Energy Ltd.'s Western Canadian operations and an upstream NG emissions intensity model. The case study upstream emissions intensity is estimated to be 3.1-4.0 gCO₂e/MJ NG compared to current best estimates of British Columbia (BC) emissions intensities of 6.2-12 gCO₂e/MJ NG and a US average estimate of 15 gCO₂e/MJ. The analysis reveals that compared to US studies, public GHG emissions data for Western Canada is insufficient as current public data satisfies only 50% of typical LCA model inputs. Company provided data closes most of these gaps (∼80% of the model inputs). We recommend more detailed data collection and presentation of government reported data such as a breakdown of vented and fugitive methane emissions by source. We propose a data collection template to facilitate improved GHG emissions intensity estimates and insight about potential mitigation strategies.

Reservoir optimized plunger lift technology reduces hydrocarbon emissions from aging gas wells

Gas well liquification is a problematic process whereby liquids collect in the wellbore and near wellbore reservoir resulting in production impedance in aging gas wells. Removal of these liquids is traditionally performed through human operated blowdown events; however, this practice results in the release of hydrocarbon emissions into the atmosphere. The removal process, called 'deliquification', can also be accomplished through the utilization of various plunger lift technologies. These allow the extraction of retained fluids from the wellbore and near-wellbore reservoir; however, these technologies vary greatly with respect to automation, intelligence, and efficacy. Here we examined the rates of production loss and the frequency of emission events in mature natural gas wells equipped with various automated plunger lift technologies. Overall, 'intelligent' plunger lift systems that base their optimization on reservoir and wellbore conditions, as opposed to standardized or scheduled operations, performed the best exhibiting a 0.13% loss of production gas to atmospheric emissions compared to a 1.37% loss of production observed from wells without a plunger lift system. Additionally, wells equipped with a next generation reservoir optimized plunger lift demonstrated a reduced rate of production decline compared to those wells without a plunger lift technology (-0.066%/day and -0.242%/day, respectively). These data have widespread implications for the operational and environmental management of a consistently increasing count of aging natural gas production wells.

Mobile Measurement System for the Rapid and Cost-Effective Surveillance of Methane and Volatile Organic Compound Emissions from Oil and Gas Production Sites

In this study, a ground-based mobile measurement system was developed to provide rapid and cost-effective emission surveillance of both methane (CH₄) and volatile organic compounds (VOCs) from oil and gas (O&G) production sites. After testing in several controlled release experiments, the system was deployed in a field campaign in the Eagle Ford basin, TX. We found fat-tail distributions for both methane and total VOC (C4-C12) emissions (e.g., the top 20% sites ranked according to methane and total VOC (C4-C12) emissions were responsible for ∼60 and ∼80% of total emissions, respectively) and a good correlation between them (Spearman's R = 0.74). This result suggests that emission controls targeting relatively large emitters may help significantly reduce both methane and VOCs in oil and wet gas basins, such as the Eagle Ford. A strong correlation (Spearman's R = 0.84) was found between total VOC (C4-C12) emissions estimated using SUMMA canisters and data reported from a local ambient air monitoring station. This finding suggests that this system has the potential for rapid emission surveillance targeting relatively large emitters, which can help achieve emission reductions for both greenhouse gas (GHG) and air toxics from O&G production well pads in a cost-effective way.

Methane flux from flowback operations at a shale gas site

We report measurements of methane (CH₄) mixing ratios and emission fluxes derived from sampling at a monitoring station at an exploratory shale gas extraction facility in Lancashire, England. Elevated ambient CH₄ mixing ratios were recorded in January 2019 during a period of cold-venting associated with a nitrogen lift process at the facility. These processes are used to clear the well to stimulate flow of natural gas from the target shale. Estimates of CH₄ flux during the emission event were made using three independent modeling approaches: Gaussian plume dispersion (following both a simple Gaussian plume inversion and the US EPA OTM 33-A method), and a Lagrangian stochastic transport model (WindTrax). The three methods yielded an estimated peak CH₄ flux during January 2019 of approximately 70 g s^-1. The total mass of CH₄ emitted during the six-day venting period was calculated to be 2.9, 4.2 ± 1.4(1σ) and 7.1 ± 2.1(1σ) tonnes CH₄ using the simple Gaussian plume model, WindTrax, and OTM-33A methods, respectively. Whilst the flux approaches all agreed within 1σ uncertainty, an estimate of 4.2 (± 1.4) tonnes CH₄ represents the most confident assessment due to the explicit modeling of advection and meteorological stability permitted using the WindTrax model. This mass is consistent with fluxes calculated by the Environment Agency (in the range 2.7 to 6.8 tonnes CH₄), using emission data provided by the shale site operator to the regulator. This study provides the first CH₄ emission estimate for a nitrogen lift process and the first-reported flux monitoring of a UK shale gas site, and contributes to the evaluation of the environmental impacts of shale gas operations worldwide. This study also provides forward guidance on future monitoring applications and flux calculation in transient emission events. Implications: This manuscript discusses atmospheric measurements near to the UK's first hydraulic fracturing facility, which has very high UK public, media, and policy interest. The focus of this manuscript is on a single week of data in which a large venting event at the shale gas site saw emissions of ~4 tonnes of methane to atmosphere, in breach of environmental permits. These results are likely to beresults are likely to be reported by the media and may influence future policy decisions concerning the UK hydraulic fracturing industry.

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.

Continuous OTM 33A Analysis of Controlled Releases of Methane with Various Time Periods, Data Rates and Wind Filters

Other test method (OTM) 33A has been used to quantify emissions from natural gas sites since it was introduced by the Environmental Protection Agency (EPA). The method relies on point source Gaussian (PSG) assumptions to estimate emissions rates from a targeted site or source. However, the method often results in low accuracy (typically ±70%, even under conducive conditions). These accuracies were verified with controlled-release experiments. Typically, controlled releases were performed for short periods (15–20 min) under atmospheric conditions that were ideal for effective plume transport. We examined three methane release rates from three distances over various periods of time ranging from seven hours to seven days. Data were recorded continuously from a stationary tower. Atmospheric conditions were highly variable and not always conducive to conventional OTM 33A calculations. OTM 33A estimates were made for 20-min periods when the mean wind direction corresponded to ±90° of the direction from the controlled release to the tower. Further analyses were performed by varying the frequency of the data, the length of the individual OTM 33A periods and the size of the wind angle used to filter data. The results suggested that different (than conventionally used) period lengths, wind filters, data acquisition frequencies and data quality filters impacted the accuracy of OTM 33A when applied to long term measurements.

Methane emissions from oil and gas platforms in the Bohai Sea, China

Although oil and gas explorations contribute to atmospheric methane (CH₄) emissions, their impact and influence along the shelf seas of China remain poorly understood. From 2012 to 2017, we conducted four ship-based surveys of CH₄ in the seawater column and boundary layer of the Bohai Sea, China, and further measured CO₂ and several meteorological parameters. The average observed CH₄ mixing ratios in the boundary layer and its concentrations in seawater column were 1950 ± 46 ppb in November 2012 (dissolved CH₄ was not observed in this survey), 2222 ± 109 ppb and 13.0 ± 5.9 nmol/L in August 2014, 2014 ± 20 ppb and 5.4 ± 1.4 nmol/L in February 2017, and 1958 ± 25 ppb and 5.3 ± 3.8 nmol/L in May 2017, respectively. The results demonstrated that the CH₄ emissions from the oil and gas platforms accounted for approximately 72.5 ± 27.0% of the increase in the background atmospheric CH₄ in the local area. The remaining emissions were attributed to land-sea air mass transportation. Conversely, the influence of the air-sea exchange was negligible, measuring within the 10^-3 ppb range. For carbon balance calibration, the mean flaring efficiency of the oil-associated gas based on the enhancement of CO₂ (ΔCO₂) and enhancement sum of CO₂ and CH₄ (ΔCO₂ + ΔCH₄) was 98.5 ± 0.5%. Furthermore, the CH₄ emission rate from the oil and gas platforms was 0.026 ± 0.017 Tg/year, which was approximately 7.2 times greater than the sea-to-air CH₄ flux over the entire Bohai Sea area. Thus, oil and gas platforms must be recognized as important artificial hotspot sources of atmospheric CH₄ in the Bohai Sea.

Application of an atmospheric tracer ratio method to estimation of PM2.5 emission rates from wheat conveying operations at a wheat pile storage facility

Particulate matter (PM) pollution is associated with adverse effects on human health and the environment. There is no designated PM_2.5 emission factor for horizontal grain conveyors. Instead, in Washington state, the air permitting agency uses an emission factor for headhouse and grain handling operations to issue permits. There is concern that this factor does not accurately represent the conveyor operations and limits the size and operation of wheat pile facilities. The primary goal of this work was to estimate the PM_2.5 emission rate (which can further be converted to an emission factor) from wheat conveying operations at a large wheat pile storage facility in eastern Washington using an atmospheric tracer ratio method, with CO₂ gas as the tracer. The field study results yield an emission rate of 5.2[Formula: see text]1.7 grams of PM_2.5 per hour and these emissions are due to the transfer point from an upper belt to a lower belt. This rate is approximately 320 times lower than the emission rate for headhouse operations which has been used previously to represent conveyor operations. The emission rate was in relatively good agreement with results of an inverse Gaussian plume model calculation of emissions using measured ambient PM_2.5 levels at a very short distance downwind of the transfer point. A consistent PM_2.5 to tracer gas ratio over the tests showed that PM_2.5 and CO₂ disperse in a similar manner and confirmed that the CO₂ tracer release was a reliable simulation of the PM_2.5 pollutant source over distances involved in the study (less than 10 meters). The results also indicate a need for the Environmental Protection Agency to develop a designated PM_2.5 emission factor for wheat conveyance.

IMPLICATIONS

There are presently no emission factors available for large wheat pile storage facilities where wheat is transferred via long horizontal conveyor belts. As a result, local and state permitting agencies use emission factors for other types of grain handling systems. In this paper, we report the first measurements of PM_2.5 emission rates (that can further be converted to emission factors using a known grain rate on the conveyor) for horizontal grain conveyors used at wheat pile storage facilities. The measured emission rate is much less than the emission rate derived from the surrogate emission factor currently used for permit purposes. This has implications for the size and operation of wheat pile storage facilities.

Research on Ecological Infrastructure from 1990 to 2018: A Bibliometric Analysis

Ecological infrastructure (EI), as the composite system on which the sustainable development of cities depends, has attracted worldwide attention. Considering refined methodologies and broad scope, researchers lacked overall understanding of research patterns and evolutionary processes on EI-related issues. In this study, we applied the bibliometric approach to describe the current situation of EI-related research, and reveal research trends. Based on the Web of Science Core Collection, the bibliometric analysis of EI-related publications from 1990 to 2018 was performed to discuss the history and present research situation of EI, and preview research prospect. The results showed that: (1) the number of EI-related publications has surged astonishingly worldwide over the last three decades; (2) countries in Europe and North America were the first to invest heavily in EI-related research, while China started later but subsequently developed rapidly; (3) the EI-related research focuses were EI-related management, methods for countering extreme meteorological phenomena, providing ecosystem services, and protecting biodiversity; and (4) the EI-related research frontiers included the design of EI, policy research on EI, role of EI in environmental governance, and research on the adaptability of EI.

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.

Cavity Ring-Down Methane Sensor for Small Unmanned Aerial Systems

We present the development, integration, and testing of an open-path cavity ring-down spectroscopy (CRDS) methane sensor for deployment on small unmanned aerial systems (sUAS). The open-path configuration used here (without pump or flow-cell) enables a low mass (4 kg) and low power (12 W) instrument that can be readily integrated to sUAS, defined here as having all-up mass of <25 kg. The instrument uses a compact telecom style laser at 1651 nm (near-infrared) and a linear 2-mirror high-finesse cavity. We show test results of flying the sensor on a DJI Matrice 600 hexacopter sUAS. The high sensitivity of the CRDS method allows sensitive methane detection with a precision of ~10-30 ppb demonstrated for actual flight conditions. A controlled release setup, where known mass flows are delivered, was used to simulate point-source methane emissions. Examples of methane plume detection from flight tests suggest that isolated plumes from sources with a mass flow as low as ~0.005 g/s can be detected. The sUAS sensor should have utility for emissions monitoring and quantification from natural gas infrastructure. To the best of our knowledge, it is also the first CRDS sensor directly deployed onboard an sUAS.

A Near-Field Gaussian Plume Inversion Flux Quantification Method, Applied to Unmanned Aerial Vehicle Sampling

The accurate quantification of methane emissions from point sources is required to better quantify emissions for sector-specific reporting and inventory validation. An unmanned aerial vehicle (UAV) serves as a platform to sample plumes near to source. This paper describes a near-field Gaussian plume inversion (NGI) flux technique, adapted for downwind sampling of turbulent plumes, by fitting a plume model to measured flux density in three spatial dimensions. The method was refined and tested using sample data acquired from eight UAV flights, which measured a controlled release of methane gas. Sampling was conducted to a maximum height of 31 m (i.e. above the maximum height of the emission plumes). The method applies a flux inversion to plumes sampled near point sources. To test the method, a series of random walk sampling simulations were used to derive an NGI upper uncertainty bound by quantifying systematic flux bias due to a limited spatial sampling extent typical for short-duration small UAV flights (less than 30 min). The development of the NGI method enables its future use to quantify methane emissions for point sources, facilitating future assessments of emissions from specific source-types and source areas. This allows for atmospheric measurement-based fluxes to be derived using downwind UAV sampling for relatively rapid flux analysis, without the need for access to difficult-to-reach areas.

Open-path cavity ring-down methane sensor for mobile monitoring of natural gas emissions

We present the design, development, and testing results of a novel laser-based cavity ring-down spectroscopy (CRDS) sensor for methane detection. The sensor is specifically oriented for mobile (i.e. vehicle deployed) monitoring of natural gas emissions from oil and infrastructure. In contrast to most commercial CRDS sensors, we employ an open-path design which allows higher temporal response and a lower power and mass package more suited to vehicle integration. The system operates in the near-infrared (NIR) at 1651 nm with primarily telecom components and includes cellular communication for wireless data transfer. Along with basic sensor design and lab testing, we present results of field measurements showing performance over a range of ambient conditions and examples of methane plume detection.

Assessing the impact of future greenhouse gas emissions from natural gas production

Greenhouse gases (GHGs) produced by the extraction of natural gas are an important contributor to lifecycle emissions and account for a significant fraction of anthropogenic methane emissions in the USA. The timing as well as the magnitude of these emissions matters, as the short term climate warming impact of methane is up to 120 times that of CO₂. This study uses estimates of CO₂ and methane emissions associated with different upstream operations to build a deterministic model of GHG emissions from conventional and unconventional gas fields as a function of time. By combining these emissions with a dynamic, techno-economic model of gas supply we assess their potential impact on the value of different types of project and identify stranded resources in various carbon price scenarios. We focus in particular on the effects of different emission metrics for methane, using the global warming potential (GWP) and the global temperature potential (GTP), with both fixed 20-year and 100-year CO₂-equivalent values and in a time-dependent way based on a target year for climate stabilisation. We report a strong time dependence of emissions over the lifecycle of a typical field, and find that bringing forward the stabilisation year dramatically increases the importance of the methane contribution to these emissions. Using a commercial database of the remaining reserves of individual projects, we use our model to quantify future emissions resulting from the extraction of current US non-associated reserves. A carbon price of at least 400 USD/tonne CO₂ is effective in reducing cumulative GHGs by 30-60%, indicating that decarbonising the upstream component of the natural gas supply chain is achievable using carbon prices similar to those needed to decarbonise the energy system as a whole. Surprisingly, for large carbon prices, the choice of emission metric does not have a significant impact on cumulative emissions.

Importance of Superemitter Natural Gas Well Pads in the Marcellus Shale

A large-scale study of methane emissions from well pads was conducted in the Marcellus shale (Pennsylvania), the largest producing natural gas shale play in the United States, to better identify the prevalence and characteristics of superemitters. Roughly 2100 measurements were taken from 673 unique unconventional well pads corresponding to ∼18% of the total population of active sites and ∼32% of the total statewide unconventional natural gas production. A log-normal distribution with a geometric mean of 2.0 kg h^-1 and arithmetic mean of 5.5 kg h^-1 was observed, which agrees with other independent observations in this region. The geometric standard deviation (4.4 kg h^-1) compared well to other studies in the region, but the top 10% of emitters observed in this study contributed 77% of the total emissions, indicating an extremely skewed distribution. The integrated proportional loss of this representative sample was equal to 0.53% with a 95% confidence interval of 0.45-0.64% of the total production of the sites, which is greater than the U.S. Environmental Protection Agency inventory estimate (0.29%), but in the lower range of other mobile observations (0.09-3.3%). These results emphasize the need for a sufficiently large sample size when characterizing emissions distributions that contain superemitters.

Hazardous Air Pollutants Associated with Upstream Oil and Natural Gas Development: A Critical Synthesis of Current Peer-Reviewed Literature

Increased energy demands and innovations in upstream oil and natural gas (ONG) extraction technologies have enabled the United States to become one of the world's leading producers of petroleum and natural gas hydrocarbons. The US Environmental Protection Agency (EPA) lists 187 hazardous air pollutants (HAPs) that are known or suspected to cause cancer or other serious health effects. Several of these HAPs have been measured at elevated concentrations around ONG sites, but most have not been studied in the context of upstream development. In this review, we analyzed recent global peer-reviewed articles that investigated HAPs near ONG operations to ( a) identify HAPs associated with upstream ONG development, ( b) identify their specific sources in upstream processes, and ( c) examine the potential for adverse health outcomes from HAPs emitted during these phases of hydrocarbon development.

Identification, spatial extent and distribution of fugitive gas migration on the well pad scale

Global methane (CH₄) emissions are becoming increasingly important due to the contribution of CH₄ to global warming. Leaking oil and gas wells can lead to subsurface CH₄ gas migration (GM), which can cause both aquifer contamination and atmospheric emissions. Despite the need to identify and quantify GM at oil and gas well pads, effective and reliable monitoring techniques are lacking. In this field study, we used CH₄ and carbon dioxide (CO₂) efflux measurements together with soil gas stable carbon isotopic signatures to identify the occurrence and to characterize the spatio-temporal migration of fugitive gas across 17 selected well pads in Northeastern British Columbia, Canada. At 13 of these sites, operators had previously reported the occurrence of GM; however, subsequent inspections based on visual, olfactory or auditory evidence only identified GM at two of these sites. Using the soil gas efflux method, evidence for GM was found at 15 of the 17 well pads with CH₄ and CO₂ effluxes ranging from 0.017 to 180μmolm^-2s^-1(0.024 to 250gCH₄m^-2d^-1) and 0.50 to 32μmolm^-2s^-1 (1.9 to 122gCO₂m^-2d^-1), respectively. Stable carbon isotopic composition was assessed at 10 of the 17 well pads with 9 well pads showing evidence of GM. The isotopic values indicated that CH₄ in soil gas was from the same origin as CH₄ in the surface casing vent flow gas. There was no correlation between CH₄ effluxes and the distance from the well head; an equal portion of elevated effluxes were detected >10m from the well head as were detected <5m from the well head. In addition, CH₄ effluxes varied temporally with values changing by up to an order of magnitude over 2h. Although the study was carried out in Northeastern British Columbia, the results are applicable on a global scale, suggesting that inspections mostly based on visual evidence (e.g. bubbling at the well head) are not reliable for the identification of GM and, that infrequent survey measurements at predefined locations close to the well head may overestimate, underestimate or even miss CH₄ effluxes. Repetitive and relatively densely spaced gas efflux measurements using a dynamic closed chamber method proved to be a useful tool for detecting GM.

Atmospheric implications of large C2-C5 alkane emissions from the U.S. oil and gas industry

Emissions of C₂-C₅ alkanes from the U.S. oil and gas sector have changed rapidly over the last decade. We use a nested GEOS-Chem simulation driven by updated 2011NEI emissions with aircraft, surface and column observations to 1) examine spatial patterns in the emissions and observed atmospheric abundances of C₂-C₅ alkanes over the U.S., and 2) estimate the contribution of emissions from the U.S. oil and gas industry to these patterns. The oil and gas sector in the updated 2011NEI contributes over 80% of the total U.S. emissions of ethane (C₂H₆) and propane (C₃H₈), and emissions of these species are largest in the central U.S. Observed mixing ratios of C₂-C₅ alkanes show enhancements over the central U.S. below 2 km. A nested GEOS-Chem simulation underpredicts observed C₃H₈ mixing ratios in the boundary layer over several U.S. regions and the relative underprediction is not consistent, suggesting C₃H₈ emissions should receive more attention moving forward. Our decision to consider only C₄-C₅ alkane emissions as a single lumped species produces a geographic distribution similar to observations. Due to the increasing importance of oil and gas emissions in the U.S., we recommend continued support of existing long-term measurements of C₂-C₅ alkanes. We suggest additional monitoring of C₂-C₅ alkanes downwind of northeastern Colorado, Wyoming and western North Dakota to capture changes in these regions. The atmospheric chemistry modeling community should also evaluate whether chemical mechanisms that lump larger alkanes are sufficient to understand air quality issues in regions with large emissions of these species.

Aerially guided leak detection and repair: A pilot field study for evaluating the potential of methane emission detection and cost-effectiveness

Novel aerial methane (CH₄) detection technologies were used in this study to identify anomalously high-emitting oil and gas (O&G) facilities and to guide ground-based "leak detection and repair" (LDAR) teams. This approach has the potential to enable a rapid and effective inspection of O&G facilities under voluntary or regulatory LDAR programs to identify and mitigate anomalously large CH₄ emissions from a disproportionately small number of facilities. This is the first study of which the authors are aware to deploy, evaluate, and compare the CH₄ detection volumes and cost-effectiveness of aerially guided and purely ground-based LDAR techniques. Two aerial methods, the Kairos Aerospace infrared CH₄ column imaging and the Scientific Aviation in situ aircraft CH₄ mole fraction measurements, were tested during a 2-week period in the Fayetteville Shale region contemporaneously with conventional ground-based LDAR. We show that aerially guided LDAR can be at least as cost-effective as ground-based LDAR, but several variable parameters were identified that strongly affect cost-effectiveness and which require field research and improvements beyond this pilot study. These parameters include (i) CH₄ minimum dectectable limit of aerial technologies, (ii) emission rate size distributions of sources, (iii) remote distinction of fixable versus nonfixable CH₄ sources ("leaks" vs. CH₄ emissions occurring by design), and (iv) the fraction of fixable sources to total CH₄ emissions. Suggestions for future study design are provided. Implications: Mitigation of methane leaks from existing oil and gas operations currently relies on on-site inspections of all applicable facilities at a prescribed frequency. This approach is labor- and cost-intensive, especially because a majority of oil and gas-related methane emissions originate from a disproportionately small number of facilities and components. We show for the first time in real-world conditions how aerial methane measurements can identify anomalously high-emitting facilities to enable a rapid, focused, and directed ground inspection of these facilities. The aerially guided approach can be more cost-effective than current practices, especially when implementing the aircraft deployment improvements discussed here.

Methane Emissions from Natural Gas Production Sites in the United States: Data Synthesis and National Estimate

We used site-level methane (CH₄) emissions data from over 1000 natural gas (NG) production sites in eight basins, including 92 new site-level CH₄ measurements in the Uinta, northeastern Marcellus, and Denver-Julesburg basins, to investigate CH₄ emissions characteristics and develop a new national CH₄ emission estimate for the NG production sector. The distribution of site-level emissions is highly skewed, with the top 5% of sites accounting for 50% of cumulative emissions. High emitting sites are predominantly also high producing (>10 Mcfd). However, low NG production sites emit a larger fraction of their CH₄ production. When combined with activity data, we predict that this creates substantial variability in the basin-level CH₄ emissions which, as a fraction of basin-level CH₄ production, range from 0.90% for the Appalachian and Greater Green River to >4.5% in the San Juan and San Joaquin. This suggests that much of the basin-level differences in production-normalized CH₄ emissions reported by aircraft studies can be explained by differences in site size and distribution of site-level production rates. We estimate that NG production sites emit total CH₄ emissions of 830 Mg/h (95% CI: 530-1200), 63% of which come from the sites producing <100 Mcfd that account for only 10% of total NG production. Our total CH₄ emissions estimate is 2.3 times higher than the U.S. Environmental Protection Agency's estimate and likely attributable to the disproportionate influence of high emitting sites.

Need for a marginal methodology in assessing natural gas system methane emissions in response to incremental consumption

Accurate quantification of methane emissions from the natural gas system is important for establishing greenhouse gas inventories and understanding cause and effect for reducing emissions. Current carbon intensity methods generally assume methane emissions are proportional to gas throughput so that increases in gas consumption yield linear increases in emitted methane. However, emissions sources are diverse and many are not proportional to throughput. Insights into the causal drivers of system methane emissions, and how system-wide changes affect such drivers are required. The development of a novel cause-based methodology to assess marginal methane emissions per unit of fuel consumed is introduced. Implications: The carbon intensities of technologies consuming natural gas are critical metrics currently used in policy decisions for reaching environmental goals. For example, the low-carbon fuel standard in California uses carbon intensity to determine incentives provided. Current methods generally assume methane emissions from the natural gas system are completely proportional to throughput. The proposed cause-based marginal emissions method will provide a better understanding of the actual drivers of emissions to support development of more effective mitigation measures. Additionally, increasing the accuracy of carbon intensity calculations supports the development of policies that can maximize the environmental benefits of alternative fuels, including reducing greenhouse gas emissions.

Evaluating Methods To Estimate Methane Emissions from Oil and Gas Production Facilities Using LES Simulations

Large-eddy simulations (LES) coupled to a model that simulates methane emissions from oil and gas production facilities are used to generate realistic distributions of meteorological variables and methane concentrations. These are sampled to obtain simulated observations used to develop and evaluate source term estimation (STE) methods. A widely used EPA STE method (OTM33A) is found to provide emission estimates with little bias when averaged over six time periods and seven well pads. Sixty-four percent of the emissions estimated with OTM33A are within ±30% of the simulated emissions, showing a slightly larger spread than the 72% found previously using controlled release experiments. A newly developed method adopts the OTM33A sampling strategy and uses a variational or a stochastic STE approach coupled to an LES to obtain a better fit to the sampled meteorological conditions and to account for multiple sources within the well pad. This method can considerably reduce the spread of the emissions estimates compared to OTM33A (92-95% within ±30% percent error), but it is associated with a substantial increase in computational cost due to the LES. It thus provides an alternative when the additional costs can be afforded to obtain more precise emission estimates.

Environmental and individual PAH exposures near rural natural gas extraction

Natural gas extraction (NGE) has expanded rapidly in the United States in recent years. Despite concerns, there is little information about the effects of NGE on air quality or personal exposures of people living or working nearby. Recent research suggests NGE emits polycyclic aromatic hydrocarbons (PAHs) into air. This study used low-density polyethylene passive samplers to measure concentrations of PAHs in air near active (n = 3) and proposed (n = 2) NGE sites. At each site, two concentric rings of air samplers were placed around the active or proposed well pad location. Silicone wristbands were used to assess personal PAH exposures of participants (n = 19) living or working near the sampling sites. All samples were analyzed for 62 PAHs using GC-MS/MS, and point sources were estimated using the fluoranthene/pyrene isomer ratio. ∑PAH was significantly higher in air at active NGE sites (Wilcoxon rank sum test, p < 0.01). PAHs in air were also more petrogenic (petroleum-derived) at active NGE sites. This suggests that PAH mixtures at active NGE sites may have been affected by direct emissions from petroleum sources at these sites. ∑PAH was also significantly higher in wristbands from participants who had active NGE wells on their properties than from participants who did not (Wilcoxon rank sum test, p < 0.005). There was a significant positive correlation between ∑PAH in participants' wristbands and ∑PAH in air measured closest to participants' homes or workplaces (simple linear regression, p < 0.0001). These findings suggest that living or working near an active NGE well may increase personal PAH exposure. This work also supports the utility of the silicone wristband to assess personal PAH exposure.

Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Measurement System Description and Mass Balance Approach

Natural gas is an abundant resource across the United States, of which methane (CH4) is the main component. About 2% of extracted CH4 is lost through leaks. The Remote Methane Leak Detector (RMLD)-Unmanned Aerial Vehicle (UAV) system was developed to investigate natural gas fugitive leaks in this study. The system is composed of three major technologies: miniaturized RMLD (mini-RMLD) based on Backscatter Tunable Diode Laser Absorption Spectroscopy (TDLAS), an autonomous quadrotor UAV and simplified quantification and localization algorithms. With a miniaturized, downward-facing RMLD on a small UAV, the system measures the column-integrated CH4 mixing ratio and can semi-autonomously monitor CH4 leakage from sites associated with natural gas production, providing an advanced capability in detecting leaks at hard-to-access sites compared to traditional manual methods. Automated leak characterization algorithms combined with a wireless data link implement real-time leak quantification and reporting. This study placed particular emphasis on the RMLD-UAV system description and the quantification algorithm development based on a mass balance approach. Early data were gathered to test the prototype system and to evaluate the algorithm performance. The quantification algorithm derived in this study tended to underestimate the gas leak rates and yielded unreliable estimations in detecting leaks under 7 × 10 − 6 m3/s (~1 Standard Cubic Feet per Hour (SCFH)). Zero-leak cases can be ascertained via a skewness indicator, which is unique and promising. The influence of the systematic error was investigated by introducing simulated noises, of which Global Positioning System (GPS) noise presented the greatest impact on leak rate errors. The correlation between estimated leak rates and wind conditions were investigated, and steady winds with higher wind speeds were preferred to get better leak rate estimations, which was accurate to approximately 50% during several field trials. High precision coordinate information from the GPS, accurate wind measurements and preferred wind conditions, appropriate flight strategy and the relative steady survey height of the system are the crucial factors to optimize the leak rate estimations.

Assessment of methane emissions from the U.S. oil and gas supply chain

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO₂ from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

Variation in Methane Emission Rates from Well Pads in Four Oil and Gas Basins with Contrasting Production Volumes and Compositions

Atmospheric methane emissions from active natural gas production sites in normal operation were quantified using an inverse Gaussian method (EPA's OTM 33a) in four major U.S. basins/plays: Upper Green River (UGR, Wyoming), Denver-Julesburg (DJ, Colorado), Uintah (Utah), and Fayetteville (FV, Arkansas). In DJ, Uintah, and FV, 72-83% of total measured emissions were from 20% of the well pads, while in UGR the highest 20% of emitting well pads only contributed 54% of total emissions. The total mass of methane emitted as a percent of gross methane produced, termed throughput-normalized methane average (TNMA) and determined by bootstrapping measurements from each basin, varied widely between basins and was (95% CI): 0.09% (0.05-0.15%) in FV, 0.18% (0.12-0.29%) in UGR, 2.1% (1.1-3.9%) in DJ, and 2.8% (1.0-8.6%) in Uintah. Overall, wet-gas basins (UGR, DJ, Uintah) had higher TNMA emissions than the dry-gas FV at all ranges of production per well pad. Among wet basins, TNMA emissions had a strong negative correlation with average gas production per well pad, suggesting that consolidation of operations onto single pads may reduce normalized emissions (average number of wells per pad is 5.3 in UGR versus 1.3 in Uintah and 2.8 in DJ).