Successful Field Application in Continuous DTS Monitoring Under Harsh Environment of SAGD Wells Using New Optical-Fiber Technology: Case Study From Canada
Bibliographic record
Abstract
Abstract The use of optical fibers in the oil and gas industry is becoming more viable for several applications, particularly in permanent reservoir monitoring, such as distributed temperature sensing (DTS) and optical pressure transducers. However, poor long-term performance of fibers, especially at elevated temperatures, is still an issue yet to be fully resolved. This problem is critically important in steam assisted gravity drainage (SAGD) applications, where wells operate in extreme conditions of high temperatures, often exceeding 250°C, as well as in high pressures within a hydrogen-rich environment. Optical fiber performance is seriously affected by many factors, including: Hydrogen ingressionThermal resistance of the materialsMechanical resistance of the fiber Exposure of optical fibers to hydrogen changes the performance of the fibers through what is referred to in the industry as "hydrogen aging" or "hydrogen darkening." Hydrogen darkening is increased absorption or light loss due to various chemical species in the glass fiber resulting from the presence of hydrogen.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".