Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR

Abstract

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.

Methane Venting at Cold Heavy Oil Production with Sand (CHOPS) Facilities Is Significantly Underreported and Led by High-Emitting Wells with Low or Negative Value

“…Using the probability of detection and uncertainty models of Conrad et al derived through parallel controlled release studies in Saskatchewan, detection sensitivities during the present flights were 0.3–1.8 kg/h (at 50% probability of detection) with single- and multipass measurement uncertainties of −65/+141% and −41/+63%, respectively. These uncertainties are consistent with those found in tests by Bell et al This detection limit is near the 1 kg/h threshold suggested as sufficient to capture all significant methane sources at upstream oil and gas facilities . Manual review of high-definition photography captured from the plane permitted detected emissions to be associated with specific emitting infrastructure on each site.…”

“…Bridger Photonics' GML is an aircraft-mounted LiDAR and camera system that leverages Wavelength Modulation Spectroscopy (WMS) to detect methane plumes between the plane and the ground and produce geolocated plume imagery at 1−2 m resolution. A detected plume's path-integrated methane 34 This detection limit is near the 1 kg/h threshold suggested as sufficient to capture all significant methane sources at upstream oil and gas facilities. 35 Manual review of high-definition photography captured from the plane permitted detected emissions to be associated with specific emitting infrastructure on each site.…”

Role of Intermittency on the Effectiveness of Methane Emissions Detection at Oil and Gas Facilities: Comparative Assessment of Optical Gas Imaging and Site-wide Surveys

“…The Fugitive Emissions Abatement Simulation Tool or FEAST uses a monte-carlo based approach to model the evolution of methane emissions and the effectiveness of leak detection and repair programs at oil and gas facilities (14,15). In brief, FEAST takes as inputs data on performance parameters of leak detection technologies obtained through controlled release tests and field trials (1,2,4,(20)(21)(22). The activity data for oil and gas facilities, including component and equipment counts, is taken from the EPA supplemental proposal -only Model Plant 4, representing well sites with major production and processing equipment and centralized production facilities, is included in this study (13).…”

“…New technologies such as aerial systems, drones, satellites, and continuous monitoring systems (CMS) promise faster and more cost-effective methane mitigation compared to conventional optical gas imaging (OGI) surveys. These new technologies have been extensively tested under controlled release conditions to evaluate performance parameters such as probability of detection curves and quantification accuracy (1)(2)(3)(4). As a result, several operators are now deploying these technologies for cost-effective leak detection and mitigation (5)(6)(7)(8)(9).…”

Informing methane emissions inventories using facility aerial measurements at midstream natural gas facilities

“…Bell et al 31 performed controlled testing with Solution 1 to assess the quantication accuracy and detection limits. The dataset included 650 individual measurement passes.…”