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Numerical Modeling of Ground Stability Around Potential Geothermal Energy Storage Wells in a Canadian Subarctic Zone

2025· article· en· W4413242341 on OpenAlexaffabout
Biao Li, Jasmin Raymond, Bin Xu

Bibliographic record

VenueJournal of Physics Conference Series · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueGeomechanica (Canada)University of CalgaryConcordia University
Fundersnot available
KeywordsBoreholeGeologyGeothermal gradientDeformation (meteorology)Geotechnical engineeringGeothermal energyPermafrostPore water pressureThermalSubarctic climateGeophysicsMeteorology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.227
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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