Publications

Methane is a powerful greenhouse gas with a 25 times higher 100-year warming potential than carbon dioxide and is a target for mitigation to achieve climate goals. To control and curb methane emissions, estimates are required from the sources and sectors which are typically generated using bottom-up methods. However, recent studies have shown that national and international bottom-up approaches can significantly underestimate emissions. In this study, we present three bottom-up approaches used to estimate methane emissions from all emission sectors in the Denver-Julesburg basin, CO, USA. Our data show emissions generated from all three methods are lower than historic measurements. A Tier 1/2 approach using IPCC emission factors estimated 2022 methane emissions of 358 Gg (0.8% of produced methane lost by the energy sector), while a Tier 3 EPA-based approach estimated emissions of 269 Gg (0.2%). Using emission factors informed by contemporary and region-specific measurement studies, emissions of 212 Gg (0.2%) were calculated. The largest difference in emissions estimates were a result of using the Mechanistic Air Emissions Simulator (MAES) for the production and transport of oil and gas in the DJ basin. The MAES accounts for changes to regulatory practice in the DJ basin, which include comprehensive requirements for compressors, pneumatics, equipment leaks, and fugitive emissions, which were implemented to reduce emissions starting in 2014. The measurement revealed that normalized gas loss is predicted to have been reduced by a factor of 20 when compared to 10-year-old normalization loss measurements and a factor of 10 less than a nearby oil and production area (Delaware basin, TX); however, we suggest that more measurements should be made to ensure that the long-tail emission distribution has been captured by the modeling. This study suggests that regulations implemented by the Colorado Department of Public Health and Environment could have reduced emissions by a factor of 20, but contemporary regional measurements should be made to ensure these bottom-up calculations are realistic.

With changing demands in regulation, understanding methane emissions from offshore oil and gas production infrastructure has become increasingly important. Reported emissions from facilities in the Gulf of Mexico range from zero to thousands of tons of methane per hour, but these is currently no clear understanding of how this range compares to expected emissions from normally operating facilities. To generate realistic emission estimates, we create two bottom-up models that simulate emissions from facilities operating in the Gulf of Mexico. We estimate type 1 prototypical facilities (typically unmanned, older, lower-producing platforms in shallow water with little processing equipment, compressors, or storage tanks) to emit an average of 13 kg CH4 h−1, which corresponds to a loss of 2.7% of the average facility production. Type 2 prototypical facilities (continuously manned, higher production and operate in deeper water with processing equipment, oil storage tanks, compressors and power generation) emit an average of 88 kg CH4 h−1, which corresponds to a loss of 2.5% of production. The average measured emission from type 1 facilities was 18 kg CH4 h−1 with a median production loss estimated at 8%. The average measured emission from type 2 facilities was 36 kg CH4 h−1 with a median production loss estimated at 2.4%. Using emission factors that consider the long-tail emission distribution partly reconciles the difference between modelled and measured emission estimates, but we suggest the current the fugitive emission estimate may be an underestimate and more data on the number and size of fugitive emissions could explain differences between the modelled and measured emission estimate. We suggest the bottom-up approach described here that uses production data coupled with facility equipment could be used to identify facilities that have abnormally large measured emissions, caused by methodological failure or larger than expected fugitive emissions, which should be targeted for further evaluation resulting in remeasurement or identification of source type so that a more accurate estimates can be made on the absolute emission.

With the increased use of natural gas, safety and environmental concerns from underground leaking natural gas pipelines are becoming more widespread. What is not well understood in leakage incidents is how the soil conditions affect gas migration behavior, making it difficult to estimate the gas distribution. To shed light on these concerns, an increased understanding of subsurface methane migration after gas release is required to support efficient leak response and effective use of available technologies. In this study, three field-scale experiments were performed at the Methane Emission Technology Evaluation Center in Colorado State University to investigate the effect of soil textural heterogeneity, soil moisture, and leak rate (0.5 and 0.85 kg/h) on methane migration caused by leaking pipelines. Subsurface methane concentrations, in addition to soil moisture and meteorological data, were collected over time. A previously validated numerical model was modified and used to understand the observed methane distribution behavior. Results of this study illustrate that the influence of soil texture, leak rate, and moisture on subsurface methane distribution is determined by the relative contribution of advection and diffusion and closely related to the distance to the leak source. Advection dominates gas transport within 1–1.5 m of the leak source, driving the migration of high concentration contours. Beyond this distance, diffusion dominates migration of lower concentration contours to the far-field. Although large leak rates initially result in faster and further gas migration, the leak rate has little influence on the diffusion dominated migration farther from the leak source. Soil moisture and texture complicate gas behavior with texture variations and elevated soil moisture conditions playing a dominant role in locally increasing methane concentrations. Scenarios highlight the importance of understanding the effects of soil moisture, texture, and leak rate on gas migration behavior in an attempt to unravel their contribution to the gas concentration within the soil environment.

Continuous emission monitoring (CM) solutions promise to detect large fugitive methane emissions in natural gas infrastructure sooner than traditional leak surveys, and quantification by CM solutions has been proposed as the foundation of measurement-based inventories. This study performed single-blind testing at a controlled release facility (release from 0.4 to 6400 g CH4/h) replicating conditions that were challenging, but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicated a 90% probability of detection (POD) of 3–30 kg CH4/h; 6 of 11 solutions achieved a POD < 6 kg CH4/h, although uncertainty was high. Four had true positive rates > 50%. False positive rates ranged from 0 to 79%. Six solutions estimated emission rates. For a release rate of 0.1–1 kg/h, the solutions' mean relative errors ranged from −44% to +586% with single estimates between −97% and +2077%, and 4 solutions' upper uncertainty exceeding +900%. Above 1 kg/h, mean relative error was −40% to +93%, with two solutions within ±20%, and single-estimate relative errors were from −82% to +448%. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicates that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting.

Aerial surveys, while effective in detecting emissions from upset conditions, face challenges in fully capturing CH4 emissions due to their temporal limitations, variability in measurements, and detection thresholds. Conversely, annual inventories submitted by operators likely don’t include emissions from failure events. This study introduces a novel methodology that utilizes the Mechanistic Air Emissions Simulator (MAES) to integrate two highly variable estimation methods: inventory and aerial methods. The proposed methodology identifies and characterizes failure events with site-specific information, thereby enhancing the accuracy of inventory programs through the so-called measurement-informed inventories (MIIs). Furthermore, it emphasizes the importance of carefully comparing instantaneous emission measurements from aerial surveys with annual average emissions reported in inventories, as they have distinct timeframes. Colorado State University (CSU) collaborated with the Colorado Department of Public Health and Environment (CDPHE) to utilize this approach to enhance reported emissions from the upstream sector in Colorado Denver-Julesburg (DJ) basin. This initiative is part of the state’s efforts to reduce emissions under the Upstream greenhouse gas (GHG) Intensity Program. The goal was to incorporate measured emissions from failure events conducted by Carbon Mapper (CM) in the simulations to derive a multiplier that rectifies for potential omissions of emissions from abnormal conditions within the oil and gas (O&amp;G) sector. To simplify the simulation process, prototypical sites were defined in conjunction with operators and are used to represent groups of O&amp;G facilities in the basin with similar configuration. The outcomes of this work indicate that inventories are likely underestimating total emissions, as an additional 16.4% of total emissions from abnormal events is estimated for the basin.

Continuous Monitoring (CM) solutions have been identified as one method to address methane emissions at oil and gas (O&amp;G) facilities, as these solutions may facilitate faster emission detection and repair than traditional survey methods. This study tested 13 CM solutions over 12 weeks using single-blind controlled testing. Controlled release rates ranged from 0.08 to 6.75 kg CH4/hr and lasted 18 minutes to 8 hours. Six solutions demonstrated 90% detection limits (DLs) within the range of controlled releases, 0.5 [0.3, 0.6] kg CH4/hr to 6.8 [5.9, 8.0] kg CH4/hr. Of the six solutions, 4 had False Positive (FP) rates of 7.8-14%, and four had False Negative rates (FN) of 8-34.1%. Compared with Ilonze et al., these results show retested solutions balancing method sensitivity and FP and FN rates. All scanning/imaging solutions had high localization (≥ 40%) precision and accuracy to the equipment unit. Eleven of 13 solutions were tested for quantification; three had a mean relative quantification error ranging from 33.4 [0.85, 66.0] % to 96.1 [63.4, 128.9] %, 95% CI for leaks between 0.1 – 1 kg CH4/hr. For larger emissions (&gt; 1 kg CH4/hr), nine solutions had a mean relative quantification error ranging from 2.8 [-20.3, 25.9] % to 32.8 [17.2, 48.5] %, 95% CI. Relative to previous studies, uncertainties in quantification estimates decreased, as did FN and FP rates, with improved detection limits for 2 of the four retested solutions. These findings highlight that continuous, rigorous testing enhances solution performance, with notable improvements observed across multiple test programs using the same test protocol.

One of the biggest risks to safety on offshore platform safety is the ignition of high-pressure natural gas streams. Currently, the size and number of fugitive emissions on offshore platforms is unknown and methods used to detect fugitives have significant shortcomings. To investigate the frequency, size, and potential impact of fugitives, a data collection exercise was conducted using incidents reported, leak survey data, and independent measurements. The size and number of fugitives on offshore facilities were simulated to investigate likely areas of safety concern. Incident reports indicate in 2021 there were 113 reports of gas leaks on 1119 offshore facilities, suggesting 0.02 fugitives per Type 1 facility (older, shallow-water platforms) and 0.31 fugitives per Type 2 facility (larger deeper-water facilities). Leak survey data report 12 fugitives per Type 1 facility (average emission 0.6 kg CH4 h−1 leak−1) and 15 fugitives per Type 2 facility (average emission 1.5 kg CH4 h−1 leak−1). Reconciliation of direct measurements with a bottom-up model suggests that the number of fugitive emissions generated from the leak report data is an underestimate for Type 1 platforms (44 fugitives facility−1; average emission 0.6 kg CH4 h−1 leak−1) and in general agreement for the Type 2 platforms (15 fugitives facility−1; average emission 1.5 kg CH4 h−1 leak−1). Analysis of the fugitive emission rates on an offshore platform suggests that gas will not collect to explosive concentration if any air movement is present (&gt;0.36 mph); however, large volumes of air (~600 m3) near representative leaks on the working deck could become explosive in hour-long zero-wind conditions. We suggest that wearable technology could be employed to indicate gas build up, safety regulations amended to consider low-wind conditions and real-world experiments are conducted to test assumptions of air mixing on the working deck.