Geophysical Electrical Monitoring of Thermal Response Tests for Complementary Thermal Property Estimation
Bibliographic record
Abstract
Thermal Response Tests (TRT) are conducted to determine the equivalent thermal conductivity and, in some cases, the volumetric heat capacity of the subsurface, which are essential for designing low-temperature geothermal systems. During TRTs, heated water is circulated through a ground heat exchanger (GHE), while the temperature variations at the borehole's inlet and outlet are monitored over time (Pasquier et al., 2016). Generally, TRT interpretation yields averaged thermal properties along the entire GHE, overlooking variations in geological materials that can affect heat transfer efficiency. Acquiring detailed thermal and hydraulic property data at varying depths allows for the optimization of GHE design to enhance overall performance. Furthermore, traditional TRTs primarily rely on water temperature measurements, providing limited information into the spatial distribution of temperature changes in the surrounding geological environment.Geophysical methods, such as electrical resistivity monitoring, can provide complementary measurements using the sensitivity of electrical resistivity to temperature changes. In this study, we investigate the use of geoelectrical monitoring during TRTs to image temperature variations in the geological environment to improve the recovery of localized thermal properties. A geoelectrical cable is placed inside the borehole during the TRT, with an electric current injected through surface and borehole electrodes. Another set of surface and borehole electrodes measures the resulting potential differences. Varying electrode spacing allows to measure apparent resistivity changes at different radial distances from the borehole. This means that electrical measurements are sensitive to temperature changes at various depths in the geological environment and could image heat transfer during the TRT. We conducted two proof-of-concept studies on standing column wells (SCW) in Varennes and Saint-Anne-des-Plaines in Québec, Canada, to test electrical monitoring during a TRT. The SCWs were subjected to heating, bleeding and recovery phases, while time-lapse electrical measurements were taken using a geoelectrical cable installed in the SCWs.Field data shows a strong correlation between apparent electrical resistivity and temperature variations during heating and recovery cycles. Geoelectrical data is compared with infinite and cylindrical line source models to simulate temperature-induced resistivity changes around the SCW. Preliminary results indicate varying sensitivity of apparent resistivity variations to SCW water temperatures, as well as to the subsurface's thermal conductivity and heat capacity. Building on these findings, the study aims to derive localized thermal parameters from the geoelectrical data. This approach highlights the potential of geophysical monitoring to enhance the accuracy of thermal characterization in TRTs.Pasquier, P., Nguyen, A., Eppner, F., Marcotte, D., & Baudron, P. (2016). Standing column wells. Advances in Ground-Source Heat Pump Systems (pp. 269–294). Elsevier. http://dx.doi.org/10.1016/B978-0-08-100311-4.00010-8
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".