Robust Probabilities of Detection and Quantification Uncertainty for Aerial Methane Detection: Examples for Three Airborne Technologies

Abstract

Thorough understanding of probabilities of detection (POD) and quantification uncertainties is fundamentally important when applying aerial measurement technologies as part of alternative means of emission limitation (AMEL) or alternate fugitive emissions management programs (Alt-FEMP), as part of monitoring, reporting, and verification (MRV) efforts, and in surveys designed to support measurement-based emissions inventories and mitigation tracking. This paper presents a robust framework for deriving continuous probability of detection functions and quantification uncertainty models for aerial measurement techniques based on controlled release data. Using extensive fully- and semi-blinded controlled release experiments to test Bridger Photonics Inc.’s Gas Mapping LiDAR (GML), as well as available release data for Kairos LeakSurveyor and NASA/JPL AVIRIS-NG technologies, robust POD functions are derived that enable calculation of detection probability for any given source rate, wind speed, and flight altitude. Uncertainty models are separately developed that independently address measurement bias, bias variability, and measurement precision, allowing for a distribution of the true source rate to be directly calculated from the source rate estimated by the technology. Derived results demonstrate the potential of all three technologies in methane detection and mitigation, and the developed methodology can be readily applied to characterize other techniques or update POD and uncertainty models following future controlled release experiments. Finally, the analyzed results also demonstrate the importance of using controlled release data from a range of sites and times to avoid underestimating measurement uncertainties.

Creating Measurement-Based Oil and Gas Sector Methane Inventories using Source-Resolved Aerial Surveys

“…Finally, this approach provides the relative contribution of each stratum to the whole, which is important data for regulation and mitigation. While the present demonstration of this approach uses aerial measurement data collected using Bridger Photonics Gas Mapping LiDAR (GML), the protocol is generally applicable to any technology with well-characterized probabilities of detection (POD) and quanti cation uncertainties 25 and su cient spatial resolution to resolve individual facilities.…”

“…Referring to Fig. 1b, this is possible via a parallel Monte Carlo simulation considering site/condition-speci c POD 25 using "bottom-up" equipment count and measurement data from prior studies (e.g., refs [29][30][31][32][33] as inputs. This new "unmeasured source" protocol allows robust derivation of stratum-dependent, average, emission factors for unmeasured sources on a per-site basis.…”

“…As outlined in Fig. 1a and more fully detailed in the SI, the measured source inventory calculation takes pass-by-pass aerial measurement data from surveyed sites, introduces known measurement uncertainties and detection sensitivities 25 via Monte Carlo and Bayesian analysis, and scales via bootstrapping to consider sites not included in the sample. This approach solves three key challenges.…”

“…Notably, this approach allows explicit consideration of the condition-speci c probability of detection during each measurement pass along with available information about a source from all passes where it had a potential to be measured. Second, the Monte Carlo analysis robustly considers the source quanti cation uncertainty during each pass, leveraging detailed uncertainty models for the aerial technology 25 , permitting direct analysis of measurement uncertainties at the source, site, and inventory level. Third, the mirror-match bootstrapping technique 26,27 enables robust scaling of emissions in each sample stratum to the population in a way that considers the actual distribution of emissions at sites in the stratum (which are generally non-smooth and highly skewed) as well as nite population effects (which are critically important since the population of facilities and wells in each stratum is nite, and the size of the sample can be large relative to the population, see Table S1 of the SI).…”

Tiered Leak Detection and Repair Programs at Oil and Gas Production Facilities

“…This dataset includes measurements that are higher than 26 kg/hr, which is a threshold typically used to refer to high-emitters (also referred to as super-emitters). 14 Bridger Photonics uses a continuous wave LiDAR measurements and has been described and evaluated by Johnson et al 24 and Conrad et al 25 From this dataset, only the emissions where operators confirmed that the source was non-routine, after following-up, were included in the distribution. Sensitivity analysis S0 was performed by adding to the distribution of close range inspections data from ERG 26 and Ravikumar et al, 27 as described by Kemp et al, 13 and assigning emissions randomly from a combined distribution, independently of which equipment the data came from, except for tanks and flares where the data comes from the flyover distribution.…”