Real-Time Fiber-Optic Casing Imager
Bibliographic record
Abstract
Abstract During the last years, significant progress has been made in the use of fiber-optic technology for well and reservoir surveillance purposes. While most effort in this field appears to be concentrated on the development of fiber-optic based temperature-, pressure- and flow meters, comparably few publications have been made to-date about the use of fiberoptic technology for monitoring deformations of well tubulars and casings. In this article we report on recent advances in our development of a real-time fiber-optic based casing imager. This device is designed for continuous, high-resolution monitoring of the shape of casings or well tubulars and, therefore, enables the determination of strain imposed on the well. Small-scale and full-casing-size laboratory tests have demonstrated that the latest generation of this system is sufficiently sensitive to detect casing deformations of less than 10 degrees per hundred feet and covers compressive and tensile axial strain ranges from less than 0.1% to 10%. We will discuss the background technology, the measurement sensitivity and strain-response characterization, as well as the scale-up work that has been performed to-date. Our article also includes an overview of field test results and illustrates how real-time deformation monitoring could form a significant component of reservoir surveillance strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".