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Get Free AccessRecent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane (CH4) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind CH4 concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the CH4 concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments.
Abhinav Anand, Stuart N. Riddick, K. B. Shonkwiler, Aashish Upreti, Michael Moy, Elijah Kiplimo, Mercy Mbua, Daniel Zimmerle (2025). Computational Fluid Dynamics-Based Modeling of Methane Flows Around Oil and Gas Equipment. , 16(7), DOI: https://doi.org/10.3390/atmos16070811.
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Type
Article
Year
2025
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.3390/atmos16070811
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