Investigation on Two M<sub>w</sub> 3.6 and M<sub>w</sub> 4.1 Earthquakes Triggered by Poroelastic Effects of Hydraulic Fracturing Operations Near Crooked Lake, Alberta
Bibliographic record
Abstract
Abstract A coupled approach of fluid flow and geomechanics is proposed in this study to quantitatively understand the hydraulic fracturing‐induced poroelastic effects that activate pre‐existing faults and trigger earthquake swarms near Crooked Lake, Alberta. The 3D poroelastic simulation of the seismogenic fault zone is then conducted to characterize the pressure diffusion and stress perturbation that led to fault activation. Results show that the poroelastic effects on the high‐permeable damage zones of a conductive‐barrier fault triggered the sequential activation of the seismogenic fault in the basement and Winterburn Formation. In addition, the high‐permeable damage zones act as conduits for pore pressure diffusion along the fault, whereas the fault core functions as a barrier to prevent crossing flow. The poroelastic effects in terms of pressure diffusion and stress perturbation in response to fluid injection facilitated the fault slip and hence triggered the M w 3.6 earthquake in the basement formation 40 days after the fracturing operations. Moreover, the mainshock created negative Coulomb failure stress changes, inhibiting further fault slip in the basement formation. Subsequently, the stimulation of another well facilitated poroelastic effects on the fault damage zone in top Winterburn Formation, reactivating the same fault with an M w 4.1 earthquake 8 days after the initiation of treatments. It is essential to optimize the injection site selection near the existing faults to reduce risks of the induced earthquakes near Crooked Lake.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".