Innovative Applications of Downhole Temperature Data
Bibliographic record
Abstract
Abstract Permanent downhole pressure and temperature gauges have been installed in many intelligent wells worldwide. They provide surveillance data about performances of the wells and the reservoirs in a fashion of high resolution and precision. Compared to the pressure data, the temperature data have been underutilized in 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 productivity index (PI) history from the temperature data. An analytical relationship between the temperature data and the PI will be presented. Using this relationship, many useful surveillance studies, such as monitoring the skin change of the well and the impact of reservoir compaction during the depletion can be conducted. Examples of such studies will be provided and discussed using some deepwater field data. Furthermore, the downhole temperature can also be used to detect whether water breakthrough occurs via matrix or fracture. By deriving the mathematical model to quantify the distance between the water front and the thermal front, we find the breakthrough via fracture usually leads to a small front distance, while the breakthrough via matrix causes a significant front distance. Coupling with the production data which includes water cut changes in producers, the temperature history reveals the real water breakthrough scenario. Observation from this analysis is of practical interest for subsea well development because of prohibitive costs and high risks of the 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".