Propagation and distribution of shear and tensile hydraulic fractures affected by frictional coal beddings
Bibliographic record
Abstract
As a typical sedimentary rock, coal seams are usually rich in bedding planes, which significantly affect the propagation process of hydraulic fracture (HF) network and reservoir permeability. In this study, the profile characteristics and the bedding roughness of the coal seam at depth of 2800 m from Daniudi Gas Field in China is investigated. The failure mechanism of shear-expansion effect under low pressure and dynamic shear under high pressure of coal beddings induced by the dissolution-corrosion effect of fracturing liquid is analyzed. The development process of HF network under different bedding angles, joint roughness coefficients (JRCs), stress differences, and confining pressures is numerically simulated by cohesive element method. Results show that the JRC of coal beddings has significant effects on the morphology of HF networks. When the JRC exceeds 15, a HF network structure with tensile main fractures and shear secondary cracks is developed. The shear fractures account for a considerable proportion (33-66%) multi-layer layered coal seams and should be taken seriously during reservoir reconstruction. As confining pressure increases, the total length of tensile HFs sharply decreases, while the change in the total length of shear HFs is not significant. When the confining pressure increases from 5 to 15 MPa, the length ratio of shear HFs to tensile hydraulic fractures increases from 0.36 to about 1, confirming that increasing confining pressure can promote the transformation of HF failure types from tension to shear. The ratio of secondary crack to main crack length is between 6.31 and 9.78, indicating the secondary fractures occupy an absolute proportion in the hydraulic fracture network. This study is of significance for understanding the hydraulic fracture network development in coal reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".