Comprehensive study of hydraulic fracturing in shale oil reservoirs comprising shale–sandstone transitions
Bibliographic record
Abstract
Hydraulic fracturing technology is a crucial technique for effectively developing shale oil reservoirs. In field fracturing treatment operations, these reservoirs are often consisting of a combination of various rock types that making them complex. Therefore, this study specially focuses on the sandstone–shale layers in the G zone of the Daqingzi Well G Area in the southern part of the Songliao Basin. It aims to provide essential parameter support for subsequent theoretical and numerical research through laboratory mechanical experiments. Using the finite discrete element method, we have established four different numerical models for the hydraulic fracturing of shale oil with varying geological conditions, (including transition zones). The study reveals that when the vertical stress difference is 6 MPa, the crack height increases, and the offset distance decreases. At 8 and 10 MPa, crack propagation exhibits a “forking” phenomenon. A decrease in rock cohesion leads to increased offset distances in the transition zone, along with an increase in crack height. For type a and b transition zones, it is recommended to use a fracturing fluid with a viscosity of approximately 10 mPa s and a flow rate of 12 m3/min for fracturing. For type c transition zones, it is advisable to select fracturing fluid with a viscosity in the range of 10–30 mPa s and use a flow rate of 12 m3/min for fracturing. For the type d transition zones in the fracturing reservoir, it is recommended to use fracturing fluid with a viscosity of around 10 mPa s and a flow rate of 15 m3/min for optimal field fracturing operations.
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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) | 0.000 | 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".