Theoretical model and experimental verification of seepage-transition-spontaneous imbibition in water migration of water-injected coal
Bibliographic record
Abstract
Coal seam water injection is widely applied to prevent mine disasters. In the traditional water migration model of water-injected coal, from ignoring capillary force in the seepage stage to generating the capillary dynamic during the spontaneous imbibition state, no transition state exists from 0 to generating capillary force. In this paper, on the basis of theoretical analysis, an equivalent capillary term is introduced to modify the Forchheimer model, and then a seepage-transition-spontaneous imbibition model that is suitable for water migration of water-injected coal is proposed. The formulas for the upper critical point velocity v 1 , lower critical point velocity v 2 , and water migration range of the transition state are derived. An analysis experiment of the spontaneous imbibition state in the coal rock water injection process and a monitoring experiment of the water injection process based on nuclear magnetic resonance method are conducted to verify the accuracy of the model. The results show that the accuracy of the water migration model, which takes the transition section into account, exceeds 0.99, representing a 12% improvement compared to the fitting precision of the water injection model that does not consider the transition section. The water migration process of water-injected coal cannot be accurately reflected without considering the transition section. In this paper, the water migration model of water-injected coal is improved, which provides theoretical support for a deeper understanding of the water injection mechanism and for enhancing the extraction efficiency of the coal seam.
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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 | 0.001 | 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".