Thermophysical properties of surficial rocks: a tool to characterize geothermal resources of remote northern regions
Bibliographic record
Abstract
Abstract The energetic framework of Canadian remote communities relies on fossil fuels. This has adverse environmental and energy security issues. In order to offset diesel consumption, the search for local, sustainable and carbon-free energy sources is of utmost importance. Unfortunately, in such remote regions, subsurface data to evaluate the geothermal potential is often nonexistent. This raises a key question: how to characterize geothermal resources associated to petrothermal systems based on surface data? Answering this question is the purpose of this work highlighting how outcrops can be used as deep subsurface analogues. The variability induced by laboratory methods to characterize thermophysical properties is further evaluated in the estimation of the present-day temperature at depth. The community of Kuujjuaq, Canada, is used as an example where guidelines are defined to evaluate the steady-state geotherm. Rock samples were collected and analyzed with a guarded heat flow meter and an optical scanner to determine thermal conductivity. Radiogenic elements concentration was evaluated with gamma-ray and mass spectrometry. 2D temperature models were built taking into account the regional geology and the results obtained from the different laboratory methods. A base-case temperature of 57–88 °C at 5 km is predicted below Kuujjuaq. This range is based on different methods used to evaluate both thermal conductivity and internal heat generation. The work conducted in Kuujjuaq shows that the combination of gamma-ray spectrometry and optical scanning gives lower base-case temperature predictions when compared to mass spectrometry combined with the guarded heat flow meter. Despite the nonexistence of deep temperature measurements in northern regions, the assessment of thermophysical properties from outcrops is shown to be a useful tool for a preliminary assessment of geothermal resources in remote areas facing critical energy issues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".