Publications

Methane, a key component of natural gas, is a potent greenhouse gas. A key feature of recent methane mitigation policies is the use of periodic leak detection surveys, typically done with optical gas imaging (OGI) technologies. The most common OGI technology is an infrared camera. In this work, we experimentally develop detection probability curves for OGI-based methane leak detection under different environmental and imaging conditions. Controlled single blind leak detection tests show that the median detection limit (50% detection likelihood) for FLIR-camera based OGI technology is about 20 g CH4/h at an imaging distance of 6 m, an order of magnitude higher than previously reported estimates of 1.4 g CH4/h. Furthermore, we show that median and 90% detection likelihood limit follows a power-law relationship with imaging distance. Finally, we demonstrate that real-world marginal effectiveness of methane mitigation through periodic surveys approaches zero as leak detection sensitivity improves. For example, a median detection limit of 100 g CH4/h is sufficient to detect the maximum amount of leakage that is possible through periodic surveys. Policy makers should take note of these limits while designing equivalence metrics for next-generation leak detection technologies that can trade sensitivity for cost without affecting mitigation priorities.

Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics’ Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1,400 kg/h. When emission rate estimates were generated using wind from high-resolution rapid refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger’s stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), the accuracy of individual emission rate estimates produced using HRRR wind ranged from −64.1% to +87.0%. Across all controlled releases, 38.1% of estimates had error within ±20%, and 87.3% of measurements were within a factor of two (−50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h), emission estimates are biased high, however when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications that must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models or to audit reported emission levels from oil and gas systems.

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 (> 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.

Accurate detection sensitivity characterization of remote methane monitoring technologies is critical for designing, implementing, and auditing effective emissions monitoring and mitigation programs. Several research groups have developed test methods based on single/double-blind controlled release protocols and regression-based data analysis techniques to create probability of detection (PoD) models for characterizing remote sensor detection sensitivities. The previously created methods and models account for some of the important factors that affect detection sensitivity, such as wind speed, and in the case of Conrad et al. flight altitude. However, these models do not account for other important factors, such as terrain albedo, variation in individual sensor performance, or spatial density of the remote sensing measurements. In this paper, we build on the work of Conrad et al. by introducing a gas concentration noise (GCN) model for Gas Mapping LiDAR aerial methane detection technology that, when combined with wind speed at the emission location, accounts for all significant sensor and environmental parameters that affect detection sensitivity for scenarios involving an isolated emission source resulting in a single methane plume. We incorporate the GCN model into Conrad et al.’s PoD model and apply it to several sets of controlled release data acquired across widely varying deployment and environmental conditions to develop PoD models for Bridger Photonics Inc.’s first- and second-generation (GML 2.0) Gas Mapping LiDAR sensors. Finally, we compare controlled release data acquired by GML 2.0 in different geographic regions and terrain cover types, in different wind conditions, deployed on different aircraft types, and with different flight parameters. Results show that the GML 2.0 PoD model remains valid regardless of the location or conditions under which the sensors are deployed, and the aircraft and flight parameters used for deployment. Based on PoD measurements in 12 production basins across North America, the average 90% PoD emission rate for sites measured by GML 2.0 in 2023 was 1.27 kg/h.

Increased interest in greenhouse gas (GHG) emissions, including recent legislative action and voluntary programs, has increased attention on quantifying, and ultimately reducing, methane emissions from the natural gas supply chain. While inventories used for public or corporate GHG policies have traditionally utilized bottom-up (BU) methods to estimate emissions, the validity of such inventories has been questioned, growing interest in utilizing full-facility measurements using airborne, satellite or drone (top- down (TD)) techniques to inform, improve, or validate inventories. This work utilized full-facility estimates from aerial measurements to evaluate and improve emissions inventories from 15 midstream natural gas facilities in the U.S.A. and represents one of the first systematic studies of measurement-informed inventory methods in this supply chain sector. At each facility, two independent, full-facility estimates were made and compared with a contemporaneous inventory assembled by the facility operator, employing comprehensive inventory methods. Estimates from the two TD methods statistically agreed in 2 of 28 paired measurements. Operator inventories, which include extensions to capture more sources and integrate local measurements, estimated significantly lower emissions than the TD estimates for 40 of 43 paired comparisons. Significant disagreement at most facilities, both between the two TD methods and between the TD estimates and enhanced inventory, indicates that using TD estimates to inform inventories (a) will require additional diagnostic screening and measurement of all sources to improve inventory estimates, and (b) that TD full-facility measurement methods require additional testing, characterization, and potentially improvement, for use in complex midstream facilities.

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.

This paper describes a new modeling toolkit for comparing the performance and electrical efficiency of AC, DC and hybrid AC/DC distribution systems for buildings, Building Electrical Efficiency Analysis Model (BEEAM). To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow along with nonlinear device behavioral descriptions derived from empirical measurements. This article describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior, device and system efficiencies using the toolkit are compared with experimental measurements, emulating a small office environment under a variety of operating and load configurations. Based on the correspondence between simulation and measurement, the toolkit is being proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC and hybrid AC/DC distribution systems in buildings.

Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH4 emissions at the facility-level. Near-field quantification of methane (CH4) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH4 h−1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.