Numerical Investigation of Hydraulic Fracture Propagation in Naturally Fractured Reservoirs Based on Lattice Spring Model
Bibliographic record
Abstract
Hydraulic fracturing has been extensively employed for permeability enhancement in low-permeability reservoirs. The geometry of the hydraulic fracture network (HFN) may have implications for the optimization of hydraulic fracturing operations. Various parameters, including the in situ stress, treatment parameters (injection rate and fluid viscosity), and orientation of natural fractures (NFs), can significantly affect the interactions between hydraulic fracture (HF) and NFs and the final HFN. In this study, a lattice-spring code was employed to determine the impact of various parameters on the geometry of the HFN. The modelling results indicated that with a large stress difference, the global orientation of the fracture propagation was restricted to the direction of maximum principal stress, and the number of fracture branches was reduced. The geometry of the HFN changed from circular to elliptical. In contrast, with an increase in the fluid viscosity/injection rate, the evolution of the geometry of the HFN exhibited the opposite trend. The global orientation of HF propagation tended to remain parallel to the direction of maximum principal stress, regardless of the branching and tortuosity of the fracture. The variations in the ratio of tensile fracture (HF) to shear fracture (shear slip on NF) can be significant, depending on the stress state, treatment parameters, and preexisting NF network, which determine the dominant stimulation mechanism. This study provides insight into the HF propagation in naturally fractured reservoirs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".