Enhancement of Coalbed Methane via Nitrogen Injection in a Coal Mining Area: A Laboratory and Field Study
Bibliographic record
Abstract
Gas flooding is a key technique for increasing coalbed methane (CBM) production of declining wells in coal mining areas. Nitrogen is clean, pollution-free, low-cost, and relatively safe compared with carbon dioxide. In this study, nitrogen drive experiments were conducted under different conditions using a coal–gas–liquid relative permeability device and gas chromatography. The effects of the temperature, intermittent time, nitrogen purity, and displacement pressure on the output characteristics were investigated, and then, field tests were conducted. The results show that with increasing displacement time, the produced gas volume and flow rate are dominated by methane at the beginning and then quickly transform into nitrogen. The volumes of both methane and nitrogen are positively correlated with the displacement time via a power function. Additionally, increasing the nitrogen temperature, intermittent time, nitrogen purity, and injection pressure can effectively increase methane production. In the field test conducted in the Fanzhuang block in the Qinshui Basin, China, the methane production of 10 monitoring wells was 11,700 m3/day, which was 2,700 m3/day higher than that before nitrogen injection. This paper provides an optimized scheme for the stimulation of CBM via nitrogen displacement in coal mining areas, which has a good application prospect.
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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 | 0.000 |
| 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".