Borehole Heat Budget Calculator: A New Tool for the Quick Exploitation of High-Resolution Temperature Profiles by Hydrogeologists
Bibliographic record
Abstract
Distributed temperature sensing is known to provide sharp signals which are very efficient for mapping hydraulically active fractures in wellbores. High-resolution temperature sensing has specifically demonstrated its capacity to characterize very low flows in wellbores. But as sharp as they can be, temperature profiles are often difficult to decipher. The aim of the present work is to provide and to test the “Borehole Heat Budget Calculator” (BHB Calculator), which is implemented as a fast and easy to use tool for the quantitative analysis of depth-temperature profiles. The Calculator is suitable for most pumping and draining configurations, as the heat budget is generalized for modelling multidirectional flow systems within the same wellbore. The formatted worksheet allows the quick exploitation of temperature logs, and is applicable for the characterization of distributed fractures in long screened wellbores. Objectives of the heat modelling are to enhance the readability of complex depth-temperature data, as well as to quantify distribution of inflow intensities and temperatures with depth. The use of heat budget helps to clearly visualize how heat conduction and heat advection contributions are distributed along wellbores profiles. Calculations of inflow temperatures and their evolution through pumping duration is a prerequisite to infer about the nature of aquifer properties (i.e. conduits, distributed or discrete fractures, porous media), as well as to give insight information about the mapping of effective flow paths draining the aquifer. The efficiency and limitations of the BHB Calculator are being tested through high-resolution temperature logging, along with complementary flowmetering and televiewing logging in fractured aquifers located in the St-Lawrence Lowlands, Quebec, Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".