Insights on Trigger Mechanisms of Two Large Hydraulic Fracturing-Induced Earthquakes and Sensitivity Analysis
Bibliographic record
Abstract
Abstract Two earthquakes with the moment magnitude of 3.9 and 4.1 occurred in January 2015 and January 2016 near the Crooked Lake region, Alberta. Both earthquakes were attributed to the hydraulic fracturing operations of three horizontal wells located at the same well-pad. The underlying mechanisms of both earthquakes are still unclear and required to be investigated to mitigate risks of future seismicity in this region. In this study, the coupled simulations of the fluid flow and geomechanics were conducted to characterize the temporal and spatial evolution of pore pressure diffusion and stress perturbation during and after hydraulic fracturing operations. The Coulomb Failure Stress along the pre-existing fault near the horizontal wells were then calculated to study the reactivation of the fault. Sensitivity analysis was finally conducted to understand the effects of the fault's orientation, injection layer permeability, and distance between the fault and hydraulic fractures on the induced seismicity. The results showed that one North-South-oriented fault was activated twice after the sequential fracturing operations of three horizontal wells in 2015 and 2016. The Mw 3.9 earthquake was triggered by the stress and pore pressure changes that activated the fault in the basement. The relatively long-time interval between the stimulation and the induced earthquake was attributed to the low permeability and geomechanics property from the injection layer to the fault. The subsequent Mw 4.1 event was triggered by the direct connection between the hydraulic fractures, natural fractures, and the fault. Sensitivity analysis has suggested that the activation of faults were susceptible to the proximity between stimulated well and seismogenic faults, low permeability of the injection layer, and the low angle between the fault strike and the maximum horizontal stress.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".