Enhanced Coalbed Methane Recovery from Lignite Using CO<sub>2</sub> and N<sub>2</sub>
Bibliographic record
Abstract
Experimental studies on enhanced coalbed methane recovery (ECBM) are crucial for advancing our understanding of gas extraction mechanisms. This investigation evaluates the feasibility of CO 2 /N 2 -ECBM application in lignite, a coal type that has received limited attention in prior ECBM studies. The adsorption characteristics of carbon dioxide, methane, and nitrogen in lignite were systematically evaluated, followed by comprehensive CO 2 /N 2 -ECBM experiments employing a self-developed experimental apparatus under various injection strategies. Key findings include the following: (1) At equivalent pressures (0.5–2.5 MPa), the adsorption capacity of carbon dioxide in lignite exceeds that of methane by a factor of 6–7, whereas nitrogen adsorption reaches 78–88% of methane levels. (2) Carbon dioxide injection predominantly occurs through matrix adsorption, achieving 70% storage efficiency with 20% displacement effectiveness. Conversely, nitrogen migration primarily follows fracture networks, yielding 20–24% storage efficiency while enhancing displacement performance. (3) Constant-pressure injection exhibits the lowest methane recovery among tested methods, particularly due to the nondrainable nature of residual methane trapped in semiclosed fracture systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".