An Integrated Method to Mitigate Hazards from Hydraulic Fracturing–Induced Earthquakes in the Duvernay Shale Play
Bibliographic record
Abstract
Summary In recent decades, a remarkable increase in induced seismicity in the Western Canada Sedimentary Basin (WCSB) has been largely attributed to hydraulic fracturing (HF) operations in unconventional plays. However, a mitigation strategy concerning geological, geomechanical, and operational susceptibilities to HF-induced seismicity has not been well understood. This work proposes an integrated method to mitigate potential risks from HF-induced seismicity in the Duvernay play near Crooked Lake. The geological susceptibility to induced seismicity is evaluated first from site-specific formation pressure and a distance to the Precambrian basement. The regional in-situ stress and rock mechanical properties are then assessed to determine the geomechanical susceptibility to induced seismicity. Next, the operational factors are determined by comparing induced seismicity with operational parameters such as total injection fluids and proppant mass. It is found that regions with a low formation pressure (<60 MPa), a great distance from the base Duvernay to the Precambrian basement (>260 m), a low minimum principal stress (<70 MPa), and a low brittleness index (<0.45) tend to be induced-seismicity-quiescent regions. Finally, a multiple linear regression (MLR)-based approach is proposed by considering the relative importance of different parameters. The MLR analysis indicates that brittleness index, formation pressure, and total injection volume are the top three controlling factors. Three new horizontal wells are drilled and the MLR analysis of these wells using the three most important parameters is conducted. High-resolution monitoring results indicated that 95% of the induced events had a local magnitude of less than 2.0 during and after the HF operations (3-month time window and 5-km well-event distance), among which the maximum magnitude reached ML3.05 (
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".