Methane emissions from abandoned oil and gas wells in Colorado.

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

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

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

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

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

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

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

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

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

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

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

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

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

Super-emitters in natural gas infrastructure are caused by abnormal process conditions.

Effectively mitigating methane emissions from the natural gas supply chain requires addressing the disproportionate influence of high-emitting sources. Here we use a Monte Carlo simulation to aggregate methane emissions from all components on natural gas production sites in the Barnett Shale production region (Texas). Our total emission estimates are two-thirds of those derived from independent site-based measurements. Although some high-emitting operations occur by design (condensate flashing and liquid unloadings), they occur more than an order of magnitude less frequently than required to explain the reported frequency at which high site-based emissions are observed. We conclude that the occurrence of abnormal process conditions (for example, malfunctions upstream of the point of emissions; equipment issues) cause additional emissions that explain the gap between component-based and site-based emissions. Such abnormal conditions can cause a substantial proportion of a site's gas production to be emitted to the atmosphere and are the defining attribute of super-emitting sites.

Reconciling divergent estimates of oil and gas methane emissions.

Published estimates of methane emissions from atmospheric data (top-down approaches) exceed those from source-based inventories (bottom-up approaches), leading to conflicting claims about the climate implications of fuel switching from coal or petroleum to natural gas. Based on data from a coordinated campaign in the Barnett Shale oil and gas-producing region of Texas, we find that top-down and bottom-up estimates of both total and fossil methane emissions agree within statistical confidence intervals (relative differences are 10% for fossil methane and 0.1% for total methane). We reduced uncertainty in top-down estimates by using repeated mass balance measurements, as well as ethane as a fingerprint for source attribution. Similarly, our bottom-up estimate incorporates a more complete count of facilities than past inventories, which omitted a significant number of major sources, and more effectively accounts for the influence of large emission sources using a statistical estimator that integrates observations from multiple ground-based measurement datasets. Two percent of oil and gas facilities in the Barnett accounts for half of methane emissions at any given time, and high-emitting facilities appear to be spatiotemporally variable. Measured oil and gas methane emissions are 90% larger than estimates based on the US Environmental Protection Agency's Greenhouse Gas Inventory and correspond to 1.5% of natural gas production. This rate of methane loss increases the 20-y climate impacts of natural gas consumed in the region by roughly 50%.

Methane Emissions from United States Natural Gas Gathering and Processing.

New facility-level methane (CH4) emissions measurements obtained from 114 natural gas gathering facilities and 16 processing plants in 13 U.S. states were combined with facility counts obtained from state and national databases in a Monte Carlo simulation to estimate CH4 emissions from U.S. natural gas gathering and processing operations. Total annual CH4 emissions of 2421 (+245/-237) Gg were estimated for all U.S. gathering and processing operations, which represents a CH4 loss rate of 0.47% (±0.05%) when normalized by 2012 CH4 production. Over 90% of those emissions were attributed to normal operation of gathering facilities (1697 +189/-185 Gg) and processing plants (506 +55/-52 Gg), with the balance attributed to gathering pipelines and processing plant routine maintenance and upsets. The median CH4 emissions estimate for processing plants is a factor of 1.7 lower than the 2012 EPA Greenhouse Gas Inventory (GHGI) estimate, with the difference due largely to fewer reciprocating compressors, and a factor of 3.0 higher than that reported under the EPA Greenhouse Gas Reporting Program. Since gathering operations are currently embedded within the production segment of the EPA GHGI, direct comparison to our results is complicated. However, the study results suggest that CH4 emissions from gathering are substantially higher than the current EPA GHGI estimate and are equivalent to 30% of the total net CH4 emissions in the natural gas systems GHGI. Because CH4 emissions from most gathering facilities are not reported under the current rule and not all source categories are reported for processing plants, the total CH4 emissions from gathering and processing reported under the EPA GHGRP (180 Gg) represents only 14% of that tabulated in the EPA GHGI and 7% of that predicted from this study.

Methane Emissions from the Natural Gas Transmission and Storage System in the United States.

The recent growth in production and utilization of natural gas offers potential climate benefits, but those benefits depend on lifecycle emissions of methane, the primary component of natural gas and a potent greenhouse gas. This study estimates methane emissions from the transmission and storage (T&S) sector of the United States natural gas industry using new data collected during 2012, including 2,292 onsite measurements, additional emissions data from 677 facilities and activity data from 922 facilities. The largest emission sources were fugitive emissions from certain compressor-related equipment and "super-emitter" facilities. We estimate total methane emissions from the T&S sector at 1,503 [1,220 to 1,950] Gg/yr (95% confidence interval) compared to the 2012 Environmental Protection Agency's Greenhouse Gas Inventory (GHGI) estimate of 2,071 [1,680 to 2,690] Gg/yr. While the overlap in confidence intervals indicates that the difference is not statistically significant, this is the result of several significant, but offsetting, factors. Factors which reduce the study estimate include a lower estimated facility count, a shift away from engines toward lower-emitting turbine and electric compressor drivers, and reductions in the usage of gas-driven pneumatic devices. Factors that increase the study estimate relative to the GHGI include updated emission rates in certain emission categories and explicit treatment of skewed emissions at both component and facility levels. For T&S stations that are required to report to the EPA's Greenhouse Gas Reporting Program (GHGRP), this study estimates total emissions to be 260% [215% to 330%] of the reportable emissions for these stations, primarily due to the inclusion of emission sources that are not reported under the GHGRP rules, updated emission factors, and super-emitter emissions.