Empirical quantification of methane emission intensity from oil and gas producers in the Permian basin
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Arizona Institutes for Resilience, University of ArizonaIssue Date
2023-01-31
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Institute of PhysicsCitation
Benjamin Hmiel et al 2023 Environ. Res. Lett. 18 024029Journal
Environmental Research LettersRights
© 2023 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Methane (CH4) emissions from the oil and natural gas (O&G) supply chain have been demonstrated to be one of the largest anthropogenic greenhouse gas emission sources ripe for mitigation to limit near-term climate warming. In recent years, exploration and production (E&P) operators have made public commitments to reducing their greenhouse gas emission intensity, yet little empirical information has been made available in the public domain to allow an accurate comparison of their emissions performance. In this study, we utilize a series of aircraft surveys of large CH4 point source emissions (∼101-104 kg CH4 hr−1) related to O&G production in the Permian Basin to enable comparison of company-level production-sector emission intensities. We calculate gas and total energy production normalized emission intensities for several of the largest E&P operators in the Permian Basin accounting for ∼85% of production within the flight region. We find differences of more than an order of magnitude in emission intensity across operators, with nearly half demonstrating a ⩾50% improvement in performance from 2019 to 2021. With the availability of such publicly attributed emissions data anticipated to increase in the future, we provide methodological insights and cautions to developing operator metrics from future empirical datasets. © 2023 The Author(s). Published by IOP Publishing Ltd.Note
Open access journalISSN
1748-9318Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1088/1748-9326/acb27e
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Except where otherwise noted, this item's license is described as © 2023 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.