A synthetical geoengineering approach to evaluate the largest hydraulic fracturing-induced earthquake in the East Shale Basin, Alberta
Bibliographic record
Abstract
On 2019-03-04, the largest induced earthquake (ML4.18) occurred in the East Shale Basin, Alberta, and the underlying physical mechanisms have not been fully understood. This paper proposes a synthetical geoengineering methodology to comprehensively characterize this earthquake caused by hydraulic fracturing. Based on 3D structural, petrophysical, and geomechanical models, an unconventional fracture model is constructed by considering the stress shadow between adjacent hydraulic fractures and the interactions between hydraulic and natural fractures. Coupled poroelastic simulations are conducted to reveal the triggering mechanisms of induced seismicity. It is found that four vertical basement-rooted faults were identified via focal mechanisms analysis. The brittleness index (BI) along two horizontal wells has a high magnitude (BI > 0.5), indicating the potential susceptibility of rock brittleness. Due to the presence of overpressure, pre-existing faults in the Duvernay Formation are highly susceptible to fault reactivation. The occurrence of the earthquake clusters has been attributed to the fracturing fluid injection during the west 38th-39th stage and east 38th stage completions. Rock brittleness, formation overpressure, and large fracturing job size account for the nucleation of earthquake clusters, and unconventional natural-hydraulic fracture networks provide fluid flow pathways to cause fault reactivation. This workflow can be used to mitigate potential seismic risks in unconventional reservoirs in other fields.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".