Coupled Poroelastic Modeling to Characterize the 4.18-Magnitude Earthquake Due to Hydraulic Fracturing in the East Shale Basin of Western Canada
Bibliographic record
Abstract
Abstract Recently, the elevated levels of seismicity activities in Western Canada have been demonstrated to be linked to hydraulic fracturing operations that developed unconventional resources. The underlying triggering mechanisms of hydraulic fracturing-induced seismicity are still uncertain. The interactions of well stimulation and geology-geomechanical-hydrological features need to be investigated comprehensively. The linear poroelasticity theory was utilized to guide coupled poroelastic modeling and to quantify the physical process during hydraulic fracturing. The integrated analysis is first conducted to characterize the mechanical features and fluid flow behavior. The finite-element simulation is then conducted by coupling Darcy's law and solid mechanics to quantify the perturbation of pore pressure and poroelastic stress in the seismogenic fault zone. Finally, the Mohr-coulomb failure criterion is utilized to determine the spatial-temporal faults activation and reveal the trigger mechanisms of induced earthquakes. The mitigation strategy was proposed accordingly to reduce the potential seismic hazards near this region. A case study of ML 4.18 earthquake in the East Shale Basin was utilized to demonstrate the applicability of the coupled modeling and numerical simulation. Results showed that one inferred fault cut through the Duvernay formation with the strike of NE20°. The fracture half-length of two wells owns an average value of 124 m. The brittleness index deriving from the velocity logging data was estimated to be a relatively higher value in the Duvernay formation, indicating a geomechanical bias of stimulated formation for the fault activation. The coupled poroelastic simulation was conducted, showing that the hydrologic connection between seismogenic faults and stimulated well was established by the end of the 38th stage completion for the east horizontal well. The simulated coulomb failure stress surrounding the fault reached a maximum of 4.15 MPa, exceeding the critical value to cause the fault slip. Hence the poroelastic effects on the inferred fault were responsible for the fault activation and triggered the subsequent ML 4.18 earthquake. It is essential to optimize the stimulation site selection near the existing faults to reduce risks of future seismic hazards near the East Shale Basin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".