A Numerical Investigation of the Potential of an Enhanced Geothermal System (EGS) for Power Generation at Mount Meager, BC, Canada
Bibliographic record
Abstract
This study aims to better harness the geothermal potential of Mount Meager in British Columbia, a premier reserve of geothermal resources in Canada. Numerical investigations explore the feasibility and optimization of an Enhanced Geothermal System to boost geothermal energy extraction capabilities. Utilizing COMSOL Multiphysics, the model simulates non-isothermal fluid flow and heat transfer through complex subsurface geology with discrete fracture planes. The sensitivity analyses assess the impact of various operational parameters, including injection strategies, reservoir characteristics, and wellbore configurations on heat extraction efficiency. These analyses indicate that a higher injection rate, lower injection temperatures, and optimized fracture areas significantly enhance system performance by maximizing thermal energy capture and minimizing thermal breakthrough. Additionally, specific wellbore configurations, particularly the triplet setup with deeper depth, significantly improve geothermal fluid circulation and heat extraction compared to doublet configurations at shallower depths. This study reveals that the base case scenario of the EGS could generate approximately 8.311× 109 kWh over 30 years, while optimization strategies could elevate potential production to up to 16.68× 109 kWh. These findings underscore the critical role of carefully designed operational strategies that leverage local geological and thermal characteristics to optimize geothermal systems, thereby enhancing efficiency and promoting sustainable energy development at Mount Meager.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".