New permeability model considering multiscale migration mechanism of deep coalbed methane and its application
Bibliographic record
Abstract
Deep coalbed methane (CBM) flow exhibits various transportation mechanisms within the multiscale pore structures of reservoirs, including continuous flow, Knudsen diffusion, and surface diffusion. Current research predominantly emphasizes the effects of individual or partial flow mechanisms and single-factor influences on the multiscale migration of CBM. We proposed a new apparent permeability model that integrates multiple flow mechanisms to enhance our understanding of the factors governing CBM flow in complex fractured networks. This model accounted for stress sensitivity, adsorbed gas desorption, and matrix shrinkage. By assigning appropriate weights to different flow mechanisms, the model yielded a more accurate representation of the deep CBM apparent permeability, avoiding the overestimation resulting from the linear superposition of diverse migration mechanisms. Our findings indicated that the apparent permeability was positively correlated with compressibility and negatively correlated with the tortuosity and Poisson's ratio. In the presence of the adsorbed gas, the apparent permeability of organic matter showed heightened sensitivity to formation pressure, rock compressibility, and tortuosity. However, the impacts of these factors became less pronounced when the pressure differential was small. The proposed model was applied to the flow simulations for a multi-fractured horizontal well within a deep coal reservoir characterized by a complex fracture network. The simulation results agreed well with the production data. We found that continuous flow was the dominant contributor to the apparent permeability of organic and inorganic matter within the coal rock, followed by Knudsen diffusion and surface diffusion. This study provided insights into the evolution of apparent permeability of CBM during development and offered valuable guidance for the analysis of CBM production dynamics, productivity forecasting, and production system design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".