Smart Pig Defect Tolerances: Quantifying the Benefits of Standard and High Resolution Pigs
Bibliographic record
Abstract
Smart pigs are used as part of an integrity management plan for oil and gas pipelines to detect metal loss defects. The pigs do not measure the defects: they collect signals from on board equipment and these signals are later analysed. Signal analysis is complex; consequently, defect sizing tolerances and confidence levels can be difficult to determine and apply in practice. They have a major effect when assessing the significance of the defect, and when calculating corrosion growth rates from the results of multiple inspections over time. This paper describes how defect sizing tolerances and confidence levels are obtained by pigging companies, and compares standard and high resolution pigs. Probability theory is used by the authors to estimate the likelihood that a defect is smaller or deeper than the reported (by the pig) value for both standard and high resolution tools. The paper also shows how these tolerances can be included in defect failure assessment and the results of multiple pig runs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".