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Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin

[Image: see text] The rapid reduction of methane emissions, especially from oil and gas (O&G) operations, is a critical part of slowing global warming. However, few studies have attempted to specifically characterize emissions from natural gas gathering pipelines, which tend to be more difficult...

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Detalles Bibliográficos
Autores principales: Yu, Jevan, Hmiel, Benjamin, Lyon, David R., Warren, Jack, Cusworth, Daniel H., Duren, Riley M., Chen, Yuanlei, Murphy, Erin C., Brandt, Adam R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648336/
https://www.ncbi.nlm.nih.gov/pubmed/36398313
http://dx.doi.org/10.1021/acs.estlett.2c00380
Descripción
Sumario:[Image: see text] The rapid reduction of methane emissions, especially from oil and gas (O&G) operations, is a critical part of slowing global warming. However, few studies have attempted to specifically characterize emissions from natural gas gathering pipelines, which tend to be more difficult to monitor on the ground than other forms of O&G infrastructure. In this study, we use methane emission measurements collected from four recent aerial campaigns in the Permian Basin, the most prolific O&G basin in the United States, to estimate a methane emission factor for gathering lines. From each campaign, we calculate an emission factor between 2.7 (+1.9/–1.8, 95% confidence interval) and 10.0 (+6.4/–6.2) Mg of CH(4) year(–1) km(–1), 14–52 times higher than the U.S. Environmental Protection Agency’s national estimate for gathering lines and 4–13 times higher than the highest estimate derived from a published ground-based survey of gathering lines. Using Monte Carlo techniques, we demonstrate that aerial data collection allows for a greater sample size than ground-based data collection and therefore more comprehensive identification of emission sources that comprise the heavy tail of methane emissions distributions. Our results suggest that pipeline emissions are underestimated in current inventories and highlight the importance of a large sample size when calculating basinwide pipeline emission factors.