Phase Field Modelling of Interactions Between Hydraulic Fractures and Natural Fractures
Bibliographic record
Abstract
Hydraulic fracturing is widely applied in unconventional reservoirs to generate fracture networks for productivity enhancement. Interactions between hydraulic fractures and natural fractures have a great impact on fracture propagation. In this study, we use a two-dimensional phase field model to investigate interactions between hydraulic fractures and different frictional or cemented fractures under different in-situ stress, injection rate, natural fracture orientation and strength. We find that with the increasing stress anisotropy, hydraulic fracture is more likely to cross natural fracture and leads to a lower fracture complexity. A moderate injection rate is conducive for complex fractures. The approaching angle between the hydraulic fracture and natural fracture impact fracture topology. Complex fractures are formed when the angles are not so steep. With the increasing strength contrast between natural fractures and the rock matrix, the material heterogeneity increases for hydraulic fractures to generate complex fractures. Compared with frictional NFs, opening stronger cemented NFs requires more pressure than hydraulic fracture propagating outside the interface. The numerical investigations in this study can provide theoretical support and design guidance for fracturing operations in complex geological conditions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".