Sealing faults and fluid-induced seismicity
Bibliographic record
Abstract
Sealing faults are nearly impermeable barriers that can form boundaries between subsurface pore-pressure domains. In hydrocarbon systems, sealing faults commonly form part of a structural trap; they are thus important elements for future storage of CO 2 and other gases in depleted reservoirs. The Triassic Montney Formation in western Canada hosts low-permeability gas reservoirs containing sealing faults that have previously been assumed to compartmentalize pressure domains. In this study, we show that the distribution of induced seismicity associated with hydraulic fracturing (HF) exhibits a statistically significant spatial correlation with zones of high lateral gradient in pore pressure. These high-gradient zones are interpreted as sealing fault systems. The largest induced seismicity sequence, including a 4.5 M L mainshock on 30 November 2018, occurred during HF treatments in two horizontal wells, between which there is an exceptionally large contrast (~10 MPa) in measured pore pressure. Numerical simulation of a simplified model of a hydraulic fracture intersecting a nearby vertical fault, followed by fault rupture using rate-and-state friction rheology, generates results that are in good agreement with observed strike-slip faulting near one of the HF wells. Our study demonstrates that sealing faults exhibit previously unrecognized behaviour that may be important for understanding induced seismicity risk. This article is part of the theme issue ‘Induced seismicity in coupled subsurface systems’.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".