Semiquantitative Applications of Downhole-Temperature Data in Subsurface Surveillance
Bibliographic record
Abstract
Summary Permanent downhole-pressure and -temperature gauges have been installed in many intelligent wells worldwide, providing high-resolution and precision surveillance data about the performance of wells and reservoirs. Compared with the pressure data, the temperature data have been underused in the petroleum industry. In this paper, we first examine the measured downhole-temperature variation caused by the Joule-Thomson effect and infer the true reservoir temperature and the skin history from the temperature data. An analytical relationship between the temperature data and the skin is presented. Through the use of this relationship, many purposeful surveillance studies, such as monitoring the skin change of the well, can be conducted. Examples of such studies will be provided and discussed using some deepwater-field data. Furthermore, the downhole temperature can be used to detect whether water breakthrough occurs by means of matrix or fracture through use of the thermal retardation factor. When coupled with the production data and pressure-falloff (PFO) tests, the downhole-temperature data can be used to estimate the water-breakthrough time. The application of this analysis is of practical interest for subsea-well development because of the prohibitive costs and high risks of production logging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".