Strike–Slip Fault Reactivation Triggered by Hydraulic-Natural Fracture Propagation during Fracturing Stimulations near Clark Lake, Alberta
Bibliographic record
Abstract
The fluid diffusion pathways through intricate hydraulic–natural fracture networks may lead to fault reactivations. However, the underlying mechanisms are still not well understood. In this article, we present case studies to investigate the strike–slip fault reactivation triggered by hydraulic–natural fracture propagation during fracturing stimulations. The unconventional fracture model (UFM) is built to simulate the real-time propagation of hydraulic fractures and the interaction between hydraulic and natural fractures. The Mohr–Columb failure criteria are employed to determine the fault reactivation under the UFM during or after fracturing stimulations. The following are found: (1) The results of triaxial stress experiments of 27 core samples for the key well determine the static Poisson’s ratio and Young’s modulus of 0.258 and 48.7 GPa, respectively. (2) The average strike azimuths of associated natural fractures are NE 55° and SW 260°. The fault is recognized with the strike azimuth of NW 15° and fault length of 1510 m. (3) The unconventional fracture model takes into account hydraulic fracture initiation, hydraulic–natural fracture interactions, and stress shadow effects. (4) The occurrence of M3.9 was due to fluid injection during the eight stage completions of H4. The actual increase in pore pressure and Coulomb failure stress reached 3.21 and 1.93 MPa, exceeding the threshold value of Fault 1 and triggering the M3.9-induced earthquake. The insights on triggering mechanisms will guide seismicity-free fracturing stimulations in future shale development.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".