Gas drainage optimization via fracture network characterization in coal mining
Bibliographic record
Abstract
The collapse of overlying strata in the goaf creates fractures that facilitate gas migration and accumulation in the upper corner, increasing the risk of gas overlimit. This study used physical simulations to analyze the distribution of the “three vertical zones” in the 11308 working face. The caved zone height is 28.5 m, and the fractured zone height is 54.4 m. The “voussoir beam” structure of mining-induced fractures was identified as the primary gas migration channel. An innovative fracture classification method was proposed to clarify their development and distribution, revealing that the low-level “rectangular terrace” acts as a gas accumulation “sweet spot,” guiding borehole extraction. Numerical simulations indicated that gas mainly accumulates in the high-level of the fracture zone (7–10 times the mining height). Based on these findings, a “low-level interception-high-level extraction” directional long borehole layout was designed, along with a dynamic balance model of “airflow-borehole extraction-gas migration.” Field application in the 11308 working face showed a 123% increase in gas extraction and a stable gas concentration of 0.08% in the upper corner, significantly reducing the risk of upper corner gas exceeding the limit. These results provide valuable guidance for managing gas risks in similar geological conditions in coal mines.
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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 | 0.001 | 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".