Probabilistic assessment of induced seismicity at the Alberta No. 1 geothermal project site
Bibliographic record
Abstract
Alberta No. 1 (ABNo1) is a geothermal project targeting deep carbonate, conglomerates, and sandstone formations in a potential production and injection zone for geothermal energy exploitation within the Municipal District of Greenview south of Grande Prairie, Alberta, Canada. In geothermal systems without a steam fraction (typically systems under 170 °C), rapid widespread pore pressure changes and slow temperature changes have led to increased deviatoric stresses, resulting in induced seismicity. A concern for the ABNo1 Geothermal Project is that anthropogenic seismicity from oil, gas, and well field fluid injection has created felt events in Alberta. Thus, at the beginning of this type of project, it is prudent to review the potential for induced seismicity. In this study, a geomechanical study of the Leduc and Granite Wash Formations, two potential geothermal fluid exploitation zones, has been undertaken based on borehole geophysics and regional injection-induced earthquake data. Determining subsurface properties such as state of stress, pore pressure, and fault properties, however, poses uncertainties in the absence of actual data from the target formations. Geomechanical analysis results (with associated uncertainties) are used to assess the potential for injection-induced earthquakes. A Monte Carlo probability analysis is employed to estimate the likelihood of slippage of the known faults close to the ABNo1 Geothermal Project. A cumulative distribution function of the critical pore pressure on each fault is derived from the local tectonic stress state and Mohr–Coulomb shear parameter analyses. The resultant probabilistic fault stability maps can serve as a baseline for future fluid injection projects in the region including wastewater disposal, hydraulic fracture stimulation, CO2 sequestration, as well as geothermal energy extraction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".