St. Lawrence Lowlands bottom-hole temperatures: various correction methods.
Bibliographic record
Abstract
The Cambro-Ordovician sedimentary basin of the St. Lawrence Lowlands (SLL) in the province of Quebec located in eastern Canada is actually under exploration for its low-temperature geothermal resources. The goal of this actual work is to better correct BHT data in order to evaluate the undisturbed subsurface temperature with more certainty and then improve the quality of the resource assessment. Very few subsurface temperature data collected in thermal equilibrium exist in the SLL basin. We realised new corrections in order to reduce uncertainty of the estimated rock formation temperature. BHTs were initially corrected following Harrison method. The maximum and minimum gradients enclosing the data corrected by Harrison method are approximately 14-37 °C/km and 14-34 °C/km for the caprocks and the reservoir rocks respectively. Horner corrections were additionally performed for temperature measurement series collected in wells where sufficient information was available. The maximum gradients deduced for BHTs corrected with Horner method are 23.0 and 24.0 °C/km for the caprocks and the reservoir rocks, respectively. The comparison of BHT data corrections for the St. Lawrence Lowlands (SLL) sedimentary basin show that the Horner method is yielding a narrower range of temperature and is potentially generating less uncertainty compared to Harrison method. Analysis of the results, with the help of a 3D model of the basin, indicate that the Cairnside and the Covey Hill formations, at depths of more than 3 km, are targets for low-temperature geothermal systems, since temperatures in these units can potentially be above 80 °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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".