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.

Quantifying non-steady state natural gas leakage from the pipelines using an innovative sensor network and model for subsurface emissions - InSENSE.

Detecting and quantifying subsurface leaks remains a challenge due to the complex nature and extent of belowground leak scenarios. To address these scenarios, monitoring and evaluating changes in gas leakage behavior over space and time are crucial for ensuring safe and efficient responses to known or potential gas leaks. This study demonstrates the capability of linking environmental and gas concentration data obtained using a low-cost, near real-time methane (CH4) detector network and an inverse gas migration model to capture and quantify non-steady state belowground natural gas (NG) leaks. The Estimating Surface Concentration Above Pipeline Emission (ESCAPE) model was modified to incorporate the impact of soil properties on gas migration. Field-scale controlled NG experiments with leakage rates ranging from 37 to 121 g/h indicate that elevated belowground near-surface (BNS) gas concentrations persist long before elevated surface concentrations are observed. On average, BNS CH4 concentrations were 20%-486% higher than surface CH4 concentrations within the monitoring radius of 4 m from the leak location. An increase in the BNS CH4 concentration was observed within 3 h as the leak rate increased from 37 to 89 g/h. However, due to the atmospheric fluctuations, any changes in surface CH4 concentrations could not be confirmed within this period. The plume area of the BNS CH4 extended approximately two times farther than that of the surface CH4 as the gas leak rate increased from 37 to 121 g/h. The estimated NG leak rates by the modified ESCAPE model agreed well with the experimental NG leak rates (m = 0.99 and R2 = 0.77), demonstrating that including soil characteristics and BNS CH4 measurements can advance estimations of non-steady NG leak rates in low and moderate NG leak rate scenarios. The CH4 detector network and model show potential as an innovative tool to improve operators' risk assessment and NG leakage response.

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.

Measuring methane emissions from abandoned and active oil and gas wells in West Virginia.

Recent studies have reported methane (CH4) emissions from abandoned and active oil and gas infrastructure across the United States, where measured emissions show regional variability. To investigate similar phenomena in West Virginia, we measure and characterize emissions from abandoned and active conventional oil and gas wells. In addition, we reconcile divergent regional CH4 emissions estimates by comparing our West Virginia emissions estimates with those from other states in the United States. We find the CH4 emission factors from 112 plugged and 147 unplugged wells in West Virginia are 0.1 g CH4 h-1 and 3.2 g CH4 h-1, respectively. The highest emitting unplugged abandoned wells in WV are those most recently abandoned, with the mean emission of wells abandoned between 1993 and 2015 of 16 g CH4 h-1 compared to the mean of those abandoned before 1993 of 3 × 10-3 g CH4 h-1. Using field observations at a historic mining area as a proxy for state-wide drilling activity in the late 19th/early 20th century, we estimate the number of abandoned wells in WV at between 60,000 and 760,000 wells. Methane emission factors from active conventional wells were estimated at 138 g CH4 h-1. We did not find an emission pattern relating to age of wells or operator for active wells, however, the CH4 emission factor for active conventional wells was 7.5 times larger than the emission factor used by the EPA for conventional oil and gas wells. Our results suggest that well emission factors for active and abandoned wells can vary within the same geologic formation and may be affected by differences in state regulations. Therefore, accounting for state-level variations is critical for accuracy in greenhouse gas emissions inventories, which are used to guide emissions reduction strategies.

Towards a climate-dependent paradigm of ammonia emission and deposition.

Existing descriptions of bi-directional ammonia (NH3) land-atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission-deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28-67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45-85) Tg N in 2008 to reach 132 (89-179) Tg by 2100.