Determining the Starting Time of CO<sub>2</sub> Injection and Its Implications for the CO<sub>2</sub>-ECBM Process
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Determining the starting time of the CO 2 injection is the key factor affecting the effect of CO 2 injection and CH 4 production. This study uses the Panyidong mine in the Huainan mining area as an example. First, fully coupled mathematical models are established. Second, the evolution law of the reservoir parameters under different starting times of CO 2 injection is visualized. Then, the starting time of the CO 2 injection is determined, and the transformation process of CH 4 displaced by CO 2 is discussed. Finally, the connotation of the continuous fluid process during the CO 2 -ECBM process is analyzed, and constructive suggestions for CO 2 injection are presented. The results show that during the CO 2 -ECBM process, the reservoir pressure and gas content greatly change near the CO 2 injection well, and gradually decrease near the CH 4 production well. The competitive adsorption of the gas is exothermic, and the reservoir temperature gradually increases. The competitive adsorption of gas causes matrix expansion, which results in a decrease in permeability. The CH 4 production rate can be increased by about 2 times, and the evolution law of the CO 2 injection rate shows little difference. If the purpose of CO 2 injection is to store CO 2, the start time of CO 2 injection may be set as the 2000th day. The CO 2 -ECBM process can be divided into three stages to characterize the importance of displacement and replacement effects in each stage. Determining the starting time of CO 2 injection and giving more desorption time to CH 4 is conducive to the expansion of pore and fracture spaces, and then gives more competitive adsorption time to gas. This study can promote the engineering implementation of the CO 2 -ECBM technology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".