Deep geothermal doublets versus deep borehole heat exchangers: A comparative study for cold sedimentary basins
Bibliographic record
Abstract
Geothermal systems installed at intermediate depths (∼1–2 km) in sedimentary basins represent an attractive option to provide low-carbon heat in cold countries, even where the geothermal gradient is low, using either closed- or open loop systems combined with heat pumps. However, the installation of such systems is costly and risky due to the uncertainty associated with the geological, hydrogeological and thermal properties of the targeted unit(s). Additionally, the performance of different types of systems is seldom compared in the literature for a given geological context. This paper presents a numerical approach to readily assess the maximum energy that could be produced by different geothermal systems (a deep borehole heat exchanger (DBHE) and three types of doublets) and compare their performance for preselection purposes. Since deep formations are often poorly characterized, the sensitivity of these systems to the most impactful site properties is evaluated. For each scenario of site properties, the systems were simulated for different operation flow rates. The maximum usable flow rate is determined from simulation results. The flow rate maximizing net energy production is used for the DBHE, while the maximum flow rate ensuring safe injection pressure has been selected for the doublets. The heating power produced per length drilled and system COP are calculated for each simulation and interpolated for the maximum usable flow rate to fairly compare the systems' performance. An illustrative case using the Bécancour area (eastern Canada) highlights that, even if the site stratigraphy and properties have been relatively well characterized previously, uncertainty regarding key properties significantly affects simulation results and, consequently, the choice of the geothermal system to install. Our numerical approach is intended as a decision-making aid in order to properly plan the installation of these expensive systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".