Fracture Competitive Propagation and Fluid Dynamic Diversion During Horizontal Well Staged Hydraulic Fracturing
Bibliographic record
Abstract
This study addresses the challenge of non-uniform fracture propagation in multi-cluster staged fracturing of horizontal wells by proposing a three-dimensional dynamic simulation method for temporary plugging fracturing, grounded in a fully coupled fluid–solid damage theory framework. A Tubing-CZM (cohesive zone model) coupling model was developed to enable real-time interaction computation of flow distribution and fracture propagation. Focusing on the Xinjiang X Block reservoir, this research systematically investigates the influence mechanisms of reservoir properties, engineering parameters (fracture spacing, number of perforation clusters, perforation friction), and temporary plugging parameters on fracture propagation morphology and fluid allocation. Our key findings include the following. (1) Increasing fracture spacing from 10 m to 20 m enhances intermediate fracture length by 38.2% and improves fracture width uniformity by 21.5%; (2) temporary plugging reduces the fluid intake heterogeneity coefficient by 76% and increases stimulated reservoir volume (SRV) by 32%; (3) high perforation friction (7.5 MPa) significantly optimizes fracture uniformity compared to low-friction (2.5 MPa) scenarios, balancing flow allocation ratios between edge and central fractures. The proposed dynamic flow diversion control criteria and quantified temporary plugging design standards provide critical theoretical foundations and operational guidelines for optimizing unconventional reservoir fracturing.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".