Thermal-Hydraulic-Mechanical Coupling Simulation of CO2 Enhanced Coalbed Methane Recovery with Regards to Low-Rank but Relatively Shallow Coal Seams
Bibliographic record
Abstract
CO2 injection technology into coal seams to enhance CH4 recovery (CO2-ECBM), therefore presenting the dual benefit of greenhouse gas emission reduction and clean fossil energy development. In order to gaze into the features of CO2 injection’s influence on reservoir pressure and permeability, the Thermal-Hydraulic-Mechanical coupling mechanism of CO2 injection into the coal seam is considered for investigation. The competitive adsorption, diffusion, and seepage flowing of CO2 and CH4 as well as the dynamic evolution of fracture porosity of coal seams are considered. Fluid physical parameters are obtained by the fitting equation using MATLAB to call EOS software Refprop. Based on the Canadian CO2-ECBM project CSEMP, the numerical simulation targeting shallow low-rank coal is carried out, and the finite element method is used in the software COMSOL Multiphysics. Firstly, the direct recovery (CBM) and CO2-ECBM are compared, and it is confirmed that the injection of CO2 has a significant improvement effect on methane production. Secondly, the influence of injection pressure and temperature is discussed. Increasing the injection pressure can increase the pressure difference in the reservoir in a short time, so as to improve the CH4 production and CO2 storage. However, the increase in gas injection pressure will also lead to the rapid attenuation of near-well reservoir permeability, resulting in the weakening of injection capacity. Also, when the injection temperature increases, the CO2 concentration is relatively reduced, and the replacement effect on CH4 is weakened, resulting in a slight decrease in CBM production and CO2 storage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".