Non-Intrusive Techniques to Monitor Internal Corrosion of Oil and Gas Pipelines
Bibliographic record
Abstract
Abstract A wide range of non-intrusive measurement techniques are available, each with strengths and weaknesses. It is important to analyze various techniques with respect to their accuracy, cost benefits, user-friendliness, remote monitoring, and limitations so that the results obtained can be effectively used in integrity management programs. This paper presents the results obtained from testing five (5) non-intrusive techniques (ultrasonic-handheld, ultrasonic-fixed, electrical probe, hydrogen permeation, and fibre-optic) by placing them individually on 6-foot long test pipes. Each pipe possessed artificially implanted 24 internal corrosion pits of different sizes and shapes; was attached with a non-intrusive monitoring technique on its external surface; was filled with brine, crude oil, and gas mixtures of H2S, CO2, and methane of various ratios; and was subjected to various temperature and pressure cycles over a period of twelve (12) years. Based on this investigation the reliability of non-intrusive monitoring techniques has been established. This paper deals exclusively with non-intrusive techniques and does not compare the non-intrusive techniques with other sensitive intrusive techniques.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".