Assessment of the Brittle–Ductile State of Major Injection and Confining Formations in the Alberta Basin
Bibliographic record
Abstract
Subsurface interaction between critically stressed seismogenic faults and anthropogenic fluid injection activities has caused several earthquakes of concern over the last decade. Proactive detection of the reverse and strike-slip faults inherent in the Alberta Basin is difficult, while identification of faults likely to become seismogenic is even more challenging. We present a conceptual framework to evaluate the seismogenic potential of undetected faults, within the stratigraphic sequence of interest, during the site-selection stage of fluid injection projects. This method uses the geomechanical properties of formations present at sites of interest and their current state of stress to evaluate whether hosted faults are likely to be brittle or ductile since the hazard posed by faults in brittle-state formations is generally significantly higher than that of faults in ductile-state formations. We used data from approximately 3100 multi-stress triaxial tests to calculate the Mogi brittle–ductile state line for 51 major injection and confining formations in the Alberta Basin and in situ stress and pore pressure data from approximately 1200 diagnostic fracture-injection tests to assess the last-known brittle–ductile state of each formation. Analysis of these data shows that the major injection formations assessed in the Alberta Basin were in a ductile state, with some confining (caprock) formations in a brittle state at the time of the stress measurements. Once current site-specific in situ stress data are available, our method enables site-specific assessment of the current brittle–ductile state of geologic formations within the zone of influence of large-volume fluid-injection projects and an evaluation of the associated potential for fault seismogenesis.
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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 | 0.002 | 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) | 0.000 | 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".