Recent interest in measuring methane (CH₄) emissions from abandoned oil and gas infrastructure has resulted in several methods being continually used to quantify point source emissions less than 200 g CH₄ h⁻¹. The choice of measurement approach depends on how close observers can come to the source, the instruments available, and the meteorological/micrometeorological conditions. As such, static chambers, dynamic chambers, Bacharach Hi Flow Sampler (BHFS) measurements, Gaussian plume (GP) modeling, and backward Lagrangian stochastic (bLs) models have all been used, but there is no clear understanding of the accuracy or precision of each method. To address this, we copy the experimental design for each of the measurement methods to make single field measurements of a known source, to simulate single measurement field protocol, and then make repeat measurements to generate an understanding of the accuracy and precision of each method. Here, we present estimates for the average percentage difference between the measured emission and the known emission for three repeat measurements, A_r, for emissions of 40 to 200 g CH₄ h⁻¹. The static chamber data were not presented because of safety concerns during the experiments. Both the dynamic chamber (A_r = −10 %, −8 %, and −10 % at emission rates of 40, 100, and 200 g CH₄ h⁻¹, respectively) and BHFS (A_r = −18 %, −16 %, and −18 %) repeatedly underestimate the emissions, but the dynamic chamber had better accuracy. The standard deviation of emissions from these direct measurement methods remained relatively constant for emissions between 40 and 200 g CH₄ h⁻¹. For the far-field methods, the bLs method generally underestimated emissions (A_r = +6 %, −6 %, and −7 %) while the GP method significantly overestimated the emissions (A_r = +86 %, +57 %, and +29 %) despite using the same meteorological and concentration data as input. Variability in wind speed, wind direction, and atmospheric stability over the 20 min averaging period are likely to propagate through to large variability in the emission estimate, making these methods less precise than the direct measurement methods. To our knowledge, this is the first time that methods for measuring CH₄ emissions from point sources between 40 and 200 g CH₄ h⁻¹ have been quantitatively assessed against a known reference source and against each other.

Methane (CH₄) is emitted during extraction, processing, and transport processes in the natural gas industry. As a powerful greenhouse gas, CH₄ releases are harmful to the environment. Operators aim to minimize methane loss, and continuous monitoring using low-cost fence-line sensors are now being developed to observe methane enhancements downwind of operations. However, it is not clear how useful these systems are and whether they can be used to quantify emissions or simply identify the presence of a leak. To investigate this, we deployed 4 calibrated low-cost sensors 30 m from emissions of known rates over a 48-h period. The aims were to determine: (1) how much of the time a fence-line system would detect a leakage event from a single, point source of the size typically seen at oil and gas production well pads and (2) how accurately a fence-line system can estimate CH₄ emissions using a relatively simple downwind dispersion method. Our results show that during a 48-h measurement period, the fence-line sensor network could detect CH₄ releases of 84 g h⁻¹ 40% of the time and 100% of the time for emissions greater than 167 g h⁻¹ using an enhancement threshold of 2 ppm. A Gaussian plume inversion based on binned centerline, maximum measured concentrations and the WindTrax Lagrangian particle model were each tested. With these models, average estimated emissions were within ±50% of a known emission rate in 24 h and ±25% in 48 h; however, estimated individual 20-min average emissions vary by more than a factor of 10. A simple Gaussian plume inversion using all of the measured concentrations produced unreasonable average emission estimates because of the inability of the equation to parameterize lateral dispersion at distances less than 100 m when the sensor was on the edge of the plume. This study provides evidence to support the use of low-cost sensors as autonomous fence-line monitoring systems to detect and potentially quantify emissions. If the sensors are properly calibrated and sensor deployment location is optimized for prevailing wind directions at each site, fence-line systems could be used routinely to quantify emissions from oil and gas infrastructure.

The United Kingdom (UK) government's National Atmospheric Emissions Inventory (NAEI) is used to provide UK greenhouse gas emission data to the United Nations Framework Convention on Climate Change. The NAEI bottom-up approach estimated 2019 methane (CH₄) emissions from the extraction and transport of oil and natural gas (O&G) from offshore sources to onshore terminals at 52 Gg CH₄, corresponding to the loss of 0.14% of gas production. Here we investigate the approaches used by the NAEI and find it substantially underestimates leakage. We suggest alternative integrated approaches that combine direct measurements, management practices, and the effects of environmental conditions to estimate fugitive emissions. We estimate the total UK CH₄ emissions from flaring, combustion, processing, venting, and O&G transfer to be 289 Gg CH₄ (0.72% of production), five times larger than the NAEI estimate. We suggest NAEI underestimates emissions because of outdated/incorrect emission factors, incomplete activity data, and incomplete data on vented emissions. Similarly, CH₄ emissions from upstream O&G in other countries may be underestimated, with regional regulatory differences resulting in venting often being a substantially underestimated emission source. While bottom-up methods can be used to understand the relative size of emissions from the extraction and transport of O&G, they are inherently biased low as they only include emissions from processes and activities designated as emission sources and for which emission factors exist. Emission factors remain a large source of uncertainty as many used to generate the NAEI inventory are taken from industrial studies or unpublished research that have not been independently validated. To improve the NAEI estimate, widespread and frequent direct measurements are needed to supplement and improve bottom-up emission estimates generated with existing emission factors and activity levels.

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

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.

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