Temperature Logging in Russia: Development History of Theory, Technology of Measurements and Interpretation Techniques
Bibliographic record
Abstract
Abstract The history of development and the state of the art of well temperature logging in Russia is described in the paper. As it is known, the first logging in oil wells were temperature ones. In 1906 D. Golubyatnikov made the first measurement of temperature distribution along the well bore using the maximal thermometer. In 1932 it was developed the first electronic well thermometer. From 1970 in practice of field reserarches the high sensitive 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. The development of theoretical and methodical basis of well thermometry was made by several groups of Moscow oil institute (now it is RGUNG), VNIINeft (Moscow), Kazan state university (Kazan) and Bashkir state university (Ufa). In Bashkir SU the model of the first industrial small-size well thermometer STL-28, theoretical basis of thermometry of transient processes in the well and formations were developed and the wide practical experience of solution of different problems in oil wells by thermometry was accumulated. 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 Bashkiria, Tataria and Western Siberia by means of well thermometry in production (flowing, rod pumping and ESP ones) and injection wells, and also during the development by the gas (air) compressor, swab and jet pump. The results of practical testing of new methods of well thermometry as "active thermometry", which is based on local inductive heating of casing on different depths and observation of the transient temperature behavior, and "infrared thermometry" for survey of "dry intervals" of the well above the liquid level. Also it is discussed the mathematical models, used at interpretation of temperature log. The most recent results are connected with quantitative interpretation of 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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".