Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
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

View Full Paper
Ask AI
Bookmark
Share

Authors

ISIrina SpivakovaNazarbayev UniversityIBI. M. BayanovNazarbayev UniversitySKShams KalamNazarbayev University

Discussion

Loading...

Member takes

Implication

This analysis demonstrates a machine learning model predicting gas adsorption in coal seams, implying enhanced methane recovery.

Key Points

  • XGBoost model predicts gas adsorption effectively, achieving an R2 over 90%, enabling precise reclamation strategies.
  • Adsorption values for CH4 and CO2 range from 0 to 905.36 scf/ton, with significant variables like pressure and coal properties considered.
  • The model was validated against unseen datasets, confirming its accuracy without extensive simulations or experiments.
  • Machine learning optimizes CO2-ECBM processes, enhancing methane recovery while facilitating efficient carbon sequestration.

Cite This Study

Spivakova et al. (2025) studied this question.

synapsesocial.com/papers/68d42725713b0b5dfea6dd2dhttps://doi.org/10.2118/227205-ms
View Full Paper
Ask AI
Bookmark
Share