Hydraulic fracture initiation and propagation mechanisms in deep coalbed methane reservoirs based on Computed Tomography image reconstruction
Bibliographic record
Abstract
• Large-scale coal samples were employed for hydraulic fracturing experiments simulating deep CBM reservoirs. • Effects of burial depth (key focus), injection rate, and in-situ stress difference coefficient on initiation and propagation mechanism of hydraulic fracturing were systematically. • investigated. • CT 3D reconstruction technology was applied to visualize fracture initiation and propagation patterns. • Distinct fracture propagation modes in deep coal seams under high-stress conditions were identified and categorized. • Key strategies for enhancing deep CBM stimulation efficiency were proposed based on experimental findings. China has great potential for deep coalbed methane (CBM) resources. Nevertheless, due to the "three high" geological conditions, traditional fracturing techniques for shallow reservoirs face adaptability bottlenecks. This study, utilizing hydraulic fracturing physical experiments in combination with Computed Tomography (CT) image reconstruction, reveals the mechanisms of fracture initiation and propagation in deep coal reservoirs under the influence of burial depth, in-situ stress difference coefficient, and fluid injection rate. The results demonstrate that: the fracturing pressure and fracturing time of the deep coal reservoirs are significantly higher than those of the shallow one. Hydraulic fractures in deep coal reservoirs are primarily micro-fractures with minimal aperture, significantly limiting the stimulation volume and range. The stimulation measures of deep coal reservoirs relies more on dense micro-fractures than the typical the macro-fractures in shallow coal reservoirs. High fluid injection rates is helpful to reduce fracturing time, increase the fracture volume and improve fracture complexity. The fracturing pressure and fracturing time decrease with the increasing in-site stress difference coefficient K. At low K values, fractures are more easily influenced by weak structural planes, while at high K values, the maximum horizontal principal stress dominates the fracture direction. In the field of hydraulic fracturing in deep CBM, it is suggested to increase the injection rate, increase the number of perforation clusters, and reduce the proppant particle size, thus to realize the fracture network with comprehensive coverage and densely connection.
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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 | 0.001 | 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".