Injection-induced fault slip assessment in Montney Formation in Western Canada
Bibliographic record
Abstract
Hydraulic stimulation to enhance energy extraction from geothermal and unconventional resources is typically accompanied by seismicity because injection changes pore pressures and temperatures, facilitating slippage of fractures and faults. Induced seismicity carries potential risk if events are large enough to damage infrastructure. The uncertainty invariably associated with the state of stress measurements and subsurface geomechanics parameters affects the analysis of fault slip and seismicity induced resulting from hydraulic fracturing. In this study, a probabilistic approach is used to assess the slip tendency of known faults crossing the compartmentalized Montney Formation of western Alberta and northeastern British Columbia. We first divide the formation into four different stress areas based on pore pressure deviations from hydrostatic. In each stress area, geomechanics parameters are expressed as probability distributions using multivariable datasets from borehole petrophysical data to injection-induced focal mechanisms. Monte Carlo simulations are applied to assess the potential slip tendency of local faults. We display the cumulative distribution function of critical pore pressure to cause slip on each fault by using analyses of the parameters of the Mohr-Coulomb shear failure criterion and local tectonic stress state. The results provide useful input for seismic hazard assessment and risk mitigation for local faults affected by high-rate fluid injection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".