Geothermal reservoir modeling and calibration in a coupled thermo-hydro-mechanical perspective - a case study for DEEP Geothermal Project in Saskatchewan, Canada
Bibliographic record
Abstract
ABSTRACT: Geothermal offers a low-emission, reliable and sustainable source of base-load energy, yet efficient reservoir management is challenging. Understanding the Thermo (T), Hydro (H) and Mechanical (M) interactions can help create sustainable strategies and ease energy extraction. In this study, a geological regional-scale THM model is created using PETREL, based on geological data from 11 wells close to the DEEP geothermal project. Geostatistical modelling was challenging as only 11 wells are available for such a large area. The depositional directions are used as secondary data for stratigraphic modeling. The permeability was populated using Machine Learning techniques based on 9 well logs as input data and calibrated with pulse-decay tests. A Mechanical Earth Model (MEM) was set up using a combination of empirical equations and calibrated using laboratory and field data. The minimum horizontal stress in the sandstone reservoir was found to be lower (~41 MPa) compared with caprock shale. Two open geothermal loop tests were history matched using INTERSECT which confirm fractures are induced for improved transmissibility in the sandstone reservoir without causing caprock failures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".