Qualitative and Quantitative Interpretation: The State of the Art in Temperature Logging
Bibliographic record
Abstract
Abstract In the paper it is described the achievements in the modern well thermometry and analyzed the problem of quantitative interpretation. It is known the first logging in oil wells was temperature one. In 1906 the professor D. Golubyatnikov on Apsheron (Azerbaijan) at first time measured the temperature distribution along the wellbore using the maximal thermometer. Today the high sensitive electronic thermometers with resolution of 0.01K are used: it is registered and analyzed the temperature changes of hundreds and tens parts of degree, caused by Joule -Thomson effect and adiabatic effect. At present time the most volume of production log is accounted to thermometry. In the paper it is given the examples of field cases from Russia by means of well thermometry during the development using the gas (air) compressor. The results of practical testing of new methods of well thermometry as "active thermometry", which is based on local inductive heating of casing on the different depths and observing the behavior of the transient temperature, are discussed. It's known that despite many attempts to develop quantitative interpretation methods, the interpretation of temperature measurements has remained mostly qualitative. The paper describes the mathematical models, used at interpretation of temperature logs. The most recent results are connected with quantitative interpretation of quasi-steady temperature distribution along the well and pressure and temperature transients with the purpose of determination of flow rates and individual parameters (for example, radius and permeability of damaged zone) of formation in multilayer wells. The application of the developed models to interpretation of temperature measurements in the different wells demonstrated on real field data sets.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".