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September 17, 2025

A Data-Driven Model for Predicting Competitive Adsorption of CH4 and CO2 in Shallow Coal Seams During CO2-Enhanced Coalbed Methane Recovery

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Authors

ISIrina SpivakovaIBI. M. BayanovSKShams Kalam

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Overview

A data-driven model predicts gas adsorption in coal seams during CO2-ECBM, suggesting improved methane recovery.

Key Points

  • XGBoost model achieved an R2 over 90%, outperforming other machine learning techniques in predicting gas adsorption.
  • The model effectively indicates that CO2 has a higher adsorption affinity than CH4, enhancing methane recovery potential.
  • Dataset analytics included operations like pressure ranging from 0 to 1507.33 psi, ensuring optimal conditions for predictions.
  • Integrating machine learning with experimental data can significantly reduce computation time in understanding gas-coal interactions.

Cite This Study

Spivakova et al. (2025) studied this question.

synapsesocial.com/papers/68d4567431b076d99fa5bcffhttps://doi.org/10.2118/227205-ms
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  1. 1Classification and Regression Trees.1984 · 23,863 citations
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  3. 3Competitive Adsorption Behavior of CO2 and CH4 in Coal Under Varying Pressures and Temperatures2025 · 7 citations