Thermal Attenuation and Lag Time in Fractured Rock: Theory and Field Measurements From Joint Heat and Solute Tracer Tests
Bibliographic record
Abstract
Abstract The modeling and prediction of heat transfer in fractured media is particularly challenging as hydraulic and transport properties depend on a multiscale structure that is difficult to resolve. In addition to advection and dispersion, heat transfer is also impacted by thermal attenuation and lag time, which results from fracture‐matrix thermal exchanges. Here we derive analytical expressions for thermal lag time and attenuation coefficient in fractured media, which quantify the effect of fracture geometry on these key factors. We use the developed expressions to interpret the results of single‐well thermal and solute tracer tests performed in a crystalline rock aquifer at the experimental site of Ploemeur (H+ observatory network). Thermal breakthrough was monitored with fiber‐optic distributed temperature sensing (FO‐DTS), which allows temperature monitoring at high spatial and temporal resolution. The observed thermal response departs from the conventional parallel plate fracture model but is consistent with a channel model representing highly channelized fracture flow. These findings, which point to a strong reduction of fracture‐matrix exchange by flow channeling, show the impact of fracture geometry on heat recovery in geothermal systems. This study also highlights the advantages to conduct both thermal and solute tracer tests to infer fracture aperture and geometry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".