A Method to Analyze the Impact of Inline Inspection Accuracy on Integrity Management Program Planning of Pipelines
Bibliographic record
Abstract
A pipeline integrity management program is greatly affected by integrity planning methods and inline inspection (ILI) tool performance. In integrity management program planning, inspection and maintenance activities are in common practice, determined from risk and integrity assessment practices with the objective to reduce risk and effectively exceed a reliability target for the safe operation of the pipeline. An efficient and effective integrity planning method can address the most significant risk and optimize the operational and maintenance costs. In this paper, a method is presented for analyzing the impact of ILI tool accuracy on integrity planning for pipelines for fatigue cracks. Crack inspection and threat of fatigue cracking was used as the working case for the analysis although the approach could potentially be used for any pipeline threat type. The proposed method is based on the use of a Monte Carlo simulation framework, where initial crack defect size and ILI measurement errors are considered as key random variables. The integrity (severity) assessment of the crack population scenarios used the CorLAS™ burst pressure model, and the Paris’ law crack growth model based on API 579. The subsequent pipeline reliability assessments also considered single and multiple cracks scenarios. Using a reliability / probability of failure (PoF) approach, the impact of ILI tool accuracy and initial crack size on when to set reinspection and reassessment intervals was investigated. Furthermore, integrity program cost scenarios for pipeline integrity programs with multiple cracks was also evaluated with respect to different (crack) populations, pipe conditions and ILI accuracies. A sensitivity analysis was performed considering different inspection costs, maintenance costs and relative crack severity for pipelines with financial metrics. Various scenarios were discussed regarding maintenance and inspection planning and a “total cost rate” for different situations. The proposed method can support integrity management program planning by linking risks with integrity plan costs associated with ILI accuracies, and optimal re-assessment intervals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".