Numerical Modeling of Ground Stability Around Potential Geothermal Energy Storage Wells in a Canadian Subarctic Zone
Bibliographic record
Abstract
Abstract Research over the past twenty years shows that borehole geothermal energy storage (BTES) systems are viable for heating buildings in cold climates, such as those found in Northern Quebec, Canada. However, BTES systems can increase temperatures in surrounding soil formations, potentially inducing ground deformation and causing unexpected accidents. This study objective is to investigate the effect of BTES systems with a 50-year service life on the mechanical response of surrounding soil formations in Northern Quebec, where potential BTES sites are covered by large quantities of over-consolidated soil formations. Fully coupled thermal-hydro-mechanical finite element modeling is conducted for cases with different configurations and operating temperatures. Processes of thermally induced pore pressure production and dissipation are simulated and analyzed, along with thermally induced large deformations of soil formations. Our results demonstrate that BTES systems with larger geometric scales or higher operating temperatures (60°C in this study) significantly influence ground responses, manifesting in changes of pore water pressure and ground deformations. Despite the marine soil in the studied subarctic region being over-consolidated, thermal disturbances can still result in plastic deformation when the maximum operating temperature reaches 60°C.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".