Impact of fracture properties on the performance of engineered geothermal systems in the crystalline basement of Kuujjuaq (Canadian Shield)
Bibliographic record
Abstract
Abstract Understanding the natural fracture network is essential for geothermal-related investigations. However, the geometrical attributes depend on the scale of observation. Therefore, a multiscale characterization of the fracture network is essential to ensure that forward heat and flow simulations are based on stochastically generated discrete fracture network models representative of the natural fracture system observed. This was the goal of this work. Fracture data was collected from satellite imagery, outcrops and well cores to evaluate the scale effect and to study the impact of fracture size and density on the performance of engineered geothermal systems by numerical modeling. The numerical simulations highlighted that networks made of small fractures (0.08 to 27 m) tend to decrease the performance of the system compared to a network made of large fractures (22 to 1,437 m). However, thermal short-circuiting is easily reached in the latter scenario. Thus, the simulations suggest that the best-case network is made of fractures ranging between 1.57 to 135 m with fractures spaced by 5 m. This scenario provides the best compromise between heat extraction, water losses, hydraulic impedance and thermal drawdown. Despite the uncertainties, the fracture data used highlights the importance of multiscale fracture analysis for heat-flow simulations of geothermal reservoirs.
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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 | 0.001 | 0.001 |
| 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".