Characterizing a Complex Induced Fracture Network: A Case Study of a Diagnostic Fracture Injection Test in the Heterogeneous Overpressured Montney Formation
Bibliographic record
Abstract
Abstract This study provides new insights into hydraulic fracture growth during a Diagnostic Fracture Injection Test (DFIT) in the presence of mechanical heterogeneity in the overpressured Montney Formation in British Columbia. A novel continuum non-local poro-elastic-plastic model of a Stimulated Rock Volume (SRV) developed in the context of the Finite Element Method is used for analysis. It is shown that DFITs in the Montney Formation can generate substantial fracture network complexity. We provide insights on potential complex fracturing patterns and distributions at the well spacing scale. Additionally, the SRV tends to span and extend several meters away on each side of the induced fracture plane. We quantify the fracture tortuosity factor which appears to deviate significantly from unity, as in the cubic law. It is further demonstrated that the aperture within the SRV can significantly drop after shut-in before it reverts and begin to heal mechanically. We show that the onset of the aperture self-healing coincides with the time when the pressure during the fall-off period becomes equal to the final effective ISIP.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".