Numerical Simulator Comparison Study for Enhanced Coalbed Methane Recovery Processes, Part I: Pure Carbon Dioxide Injection
Bibliographic record
Abstract
Abstract The injection of carbon dioxide (CO2) in deep, unmineablecoalbeds can enhance the recovery of coalbed methane (CBM) and at the same timeit is a very attractive option for geologic CO2 storage asCO2 is strongly adsorbed onto the coal. Existing CBM numerical simulators which are developed for the primary CBMrecovery process, have many important features such as: (1) a dual porositysystem; (2) Darcy flow in the natural fracture system; (3) pure gas diffusionand adsorption in the primary porosity system; and (4) coal shrinkage due togas desorption; taken into consideration. However, process mechanisms becomemore complex with CO2 injection. Additional features such as: (1)coal swelling due to CO2 adsorption on coal; (2) mixed gasadsorption; (3) mixed gas diffusion; and (4) non-isothermal effect for gasinjection; have to be considered. This paper describes the first part of a comparison study between numericalsimulators for enhanced coalbed methane (ECBM) recovery with pureCO2 injection. The problems selected for comparison are intended toexercise many of the features of CBM simulators that are of practical andtheoretical interest and to identify areas of improvement for modeling of theECBM process. The first problem set deals with a single well test withCO2 injection and the second problem set deals with ECBM recoveryprocess with CO2 injection in an inverted five-spot pattern.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".