Online Detection of Coiled Tubing Anomalies on a Small Scale for Safe CT Operations
Bibliographic record
Abstract
Abstract Since the combination of measuring fatigue and corrosion in one system would be ideal but not possible for now, the best way to ‘guard’ the coiled tubing's integrity before or during operation, is to detect corrosion or other anomalies in a very early stage. Calculations on form and depth of CT anomalies to predict its growth and behavior still turn out to be very difficult. This means online detecting on a very small scale would give you a better and immediate view on the growing speed of anomalies and thus your total decay of the CT string. ROSEN does this by means of her well-known knowledge in the Magnetic Flux Leakage principle and her 20+ years experience in pipeline inspections. For Hibernia Canada we introduced a new feature in our software concentrating on corrosion finding and wall thickness. We have divided the wall thickness into 9 different wall thickness part sections around the circumference. This means a more precise indication (and therefore better resolution) of the wall thickness part (40° coverage each), which could contain a wall thinning including its clock position. Mechanical damage, corrosion parts or pits as small as approx 0.3mm (0.01″) in depth can now be detected!
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".