Optimized inspection of upstream oil and gas methane emissions using airborne LiDAR surveillance

Rashid, Kashif; Speck, A.; Osedach, Timothy P.; Perroni, Dominic V.; Pomerantz, Andrew E.

doi:10.1016/j.apenergy.2020.115327

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…In recent years, a range of potential detection and/or measurement technologies have been explored with promise to significantly reduce time and labour costs to find and measure sources of methane, especially for applications in the oil and gas sector (Bell et al, 2020;Fox et al, 2019;Kemp and Ravikumar, 2021;Rashid et al, 2020;Ravikumar et al, 2019;Schwietzke et al, 2019). Of particular interest are airplane-mounted technologies, which are increasingly used in large-scale field campaigns with success (Chen et al, 2022;Cusworth et al, 2021;Tyner and Johnson, 2021) and gaining acceptance in alternate fugitive emissions management programs (Alt-FEMP) replacing or supplementing optical gas imaging (OGI) surveys using hand-held infrared cameras (AER, 2021;Bridger Photonics, 2022;InvestableUniverse, 2021;Kairos Aerospace, 2022a).…”

Section: Introductionmentioning

confidence: 99%

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

Conrad¹,

Tyner²,

Johnson³

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Conrad¹,

Tyner²,

Johnson³

2023

Preprint

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“…35,43,74,85,86,94 Airborne Lidar methods are active methods that are ideal for quickly sampling large areas for unknown sources and to characterize regional patterns in background and anthropogenic emissions. 84,95,96 The IPDA Lidar (Ball Aerospace & Technologies Corp, Boulder, CO, USA) provided one of the first opportunities to the measure regional and temporal variability in background concentrations, which had largely been restricted to ground-based methods and modeling. 84 Aircraft vibrations creating stripes in the Lidar data, determining the ideal sampling beam diameter, and dependence on accurate geolocation data are weaknesses of the method.…”

Section: External Tracer and Dispersion Modelsmentioning

confidence: 99%

Recent Advances Toward Transparent Methane Emissions Monitoring: A Review

Erland

Thorpe

Gamon

2022

Environ. Sci. Technol.

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Given that anthropogenic greenhouse gas (GHG) emissions must be immediately reduced to avoid drastic increases in global temperature, methane emissions have been placed center stage in the fight against climate change. Methane has a significantly larger warming potential than carbon dioxide. A large percentage of methane emissions are in the form of industry emissions, some of which can now be readily identified and mitigated. This review considers recent advances in methane detection that allow accurate and transparent monitoring, which are needed for reducing uncertainty in source attribution and evaluating progress in emissions reductions. A particular focus is on complementary methods operating at different scales with applications for the oil and gas industry, allowing rapid detection of large point sources and addressing inconsistencies of emissions inventories. Emerging airborne and satellite imaging spectrometers are advancing our understanding and offer new top-down assessment methods to complement bottom-up methods. Successfully merging estimates across scales is vital for increased certainty regarding greenhouse gas emissions and can inform regulatory decisions. The development of comprehensive, transparent, and spatially resolved top-down and bottom-up inventories will be crucial for holding nations accountable for their climate commitments.

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“…Two methods based on the Gaussian diffusion equation and transection integration were developed, respectively. Data from aircraft measurements are analyzed by the transection method described in the next section, and optimizing flying routes that affect the estimation attracted research attention [54]. The high cost of aircraft maintenance makes this measurement plan expensive, and limitations, such as no-flight zones and a minimum flight height also restrict the application of this technology.…”

Section: Type2: Methane Emission Measurement Of Facilities and Stationsmentioning

confidence: 99%

Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation

Sun

2021

Sustainability

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The emission estimation of the oil and gas sector, which involves field test measurements, data analysis, and uncertainty estimation, precedes effective emission mitigation actions. A systematic comparison and summary of these technologies and methods are necessary to instruct the technology selection and for uncertainty improvement, which is not found in existing literature. In this paper, we present a review of existing measuring technologies, matching data analysis methods, and newly developed probabilistic tools for uncertainty estimation and try to depict the process for emission estimation. Through a review, we find that objectives have a determinative effect on the selection of measurement technologies, matching data analysis methods, and uncertainty estimation methods. And from a systematic perspective, optical instruments may have greatly improved measurement accuracy and range, yet data analysis methods might be the main contributor of estimation uncertainty. We suggest that future studies on oil and gas methane emissions should focus on the analysis methods to narrow the uncertainty bond, and more research on uncertainty generation might also be required.

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Optimized inspection of upstream oil and gas methane emissions using airborne LiDAR surveillance

Cited by 12 publications

References 27 publications

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

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

Recent Advances Toward Transparent Methane Emissions Monitoring: A Review

Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation

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