Assessing the snow-curtain period ground surface temperature survey data of the Mount Meager and investigating factors influencing thermal breakthrough of the Regina Geothermal Project
Bibliographic record
Abstract
In an era characterized by the urgent need for sustainable energy solutions, geothermal energy emerges as a promising avenue for meeting growing global energy demands while reducing carbon emissions. Leveraging innovative geothermal exploration methods during the 2021 and 2022 summer fieldwork seasons in Mount Meager and Mount Cayley geothermal areas, near surface ground temperature datasets were meticulously collected. Current research endeavors focus on analyzing and evaluating these temperature records through the development of numerical models for assessing the heat balance of the shallow underground and analytical heat conservation models. Energy, Mines and Resources Canada (EMR) provided funding for the drilling of a geothermal test well on the University of Regina. Ongoing investigations are focused on meticulously analyzing and evaluating numerical models to simulate geothermal energy extraction in Regina. This thesis focuses to utilize a numerical modeling approach to enhance understanding of the heat balance system during winter's snow covering period and to analyze temperature data for calculating subsurface geothermal heat flux. The insights garnered from this endeavor are anticipated to significantly contribute to field data analysis, thereby enhancing the assessment of geothermal resources in the Mount Meager area. To gain a comprehensive grasp of temperature variation in the datasets, temperature time series recorded during the 2020-2022 winter period were classified into three groups based on measured temperature range and variation trends. Additionally, a simplified 2D numerical model, developed using COMSOL Multiphysics software, is employed to discern the heat flux range and temperature distribution for the targeted area, with a focus on identifying sensitivities to geological and environmental factors impacting ground temperature. Furthermore, this thesis aims to enhance the understanding of the thermal coupling mechanisms involved in effective geothermal energy extraction with one injector and one producer in the Regina area. A simplified 3D geological model, constructed using the same software, is employed to simulate bottomhole temperature during the geothermal production process. The model is used to identify key factors such as injection rate, reservoir rock density, thermal conductivity, and well spacing that significantly influence the thermal breakthrough time, providing insights into optimizing the efficiency of geothermal systems.
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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 | 0.001 | 0.001 |
| 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 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".