Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada
Bibliographic record
Abstract
Abstract Anticosti Island is located in the Anticosti sedimentary basin, an Ordovician/Silurian carbonate platform. This platform is mainly composed of limestone and shale with some dolomite and sandstone and reaches up to 5 km depth in the southwest. It overlies a Precambrian basement of the Grenville Province made of magmatic and metamorphic rocks. Like most remote and off-grid regions in Canada, it relies heavily on fossil fuels for energy supplies. An assessment of deep geothermal resources was achieved in this area with the objective of diversifying energy resources to help develop renewable energy for villages deserved by micro-grid systems. Despite sparse and low-quality bottom-hole data (15 wells of 1111 m to 2762 m depth), a 3D temperature model was developed for this sedimentary basin and its underlying Precambrian basement up to 40 km (mantle depth). Quantifying confidence intervals for thermal parameters, namely bottom-hole temperature, thermal conductivity, heat generation rate and mantle heat flux, was paramount to obtain a reliable range of temperature predictions. A high variability of modeled temperature, up to 41% at the base of the sedimentary basin and 70% at mantle depth, remains when trying to constrain input parameters. The lack of equilibrium temperature measurements at depth affects the temperature predictions, both in the sedimentary basin and the Precambrian basement. It is an important issue to solve in further studies. Furthermore, knowledge of the thermal properties of the Precambrian basement of the Grenville Province and its geometry is poor. In addition, there is a wide confidence interval on thermal conductivity of specific lithologies in the Anticosti sedimentary basin. It has a significant impact on temperature predictions at depth and should be improved for studies focusing on electricity production. Despite a wide confidence interval on temperature predictions, geothermal electricity generation from reservoirs at 120 °C or more appears difficult in the current technical and economic context. Electricity generation at a low temperature with an inlet of 70 °C could be achieved at a reservoir depth of 2–4 km, but with a net efficiency of 10–11% (considering a flow rate of 40 l s−1 and a cooling temperature of 5 °C). Direct use of geothermal heat from the deepest part of the sedimentary basin seems to be the most realistic option, provided that sufficiently permeable horizons can be found.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".