Permanent Distributed Temperature Sensing (DTS) Technology Applied In Mature Fields: A Forties Field Case Study
Bibliographic record
Abstract
Abstract Permanent distributed temperature sensing (DTS) using fiber-optic technology provides measurements over the complete length of the fiber in the wellbore. The temperature profiles can be monitored at surface in real time, minimizing the need for production logs, preventing deferred production losses, decreasing well interventions, and reducing operating costs. This technology has been applied by Apache North Sea Ltd in the Forties field to monitor and optimize the performance of two wells producing by gas lift in the Delta platform and, at the same time, examine their completion integrity. To accomplish these objectives, a hybrid fiber-optic electrical cable was installed in two Forties wells, allowing the continuous measurement of temperature and acquisition of pressure data from a downhole gauge located below the deepest gas-injection point. The combined benefit of reducing both the number of well interventions, and thus eliminating the associated QHSE risks, and the operating costs made this well monitoring strategy the appropriate one in this mature field. The analysis and interpretation of downhole pressure and DTS data provided rapid feedback to the platform production team regarding the status of the well, allowing a better and more informed decision-making process. In this paper, we outline the deployment of the hybrid DTS system and describe the analysis performed in each of the two wells. Data handling, analysis, and interpretation are described as well as the methodology and workflow for well monitoring and optimization using permanently installed DTS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".