Validate Crack Assessment Models With In-Service and Hydrotest Failures
Bibliographic record
Abstract
Crack or crack-like anomaly is one of the major threats to the safety and structural integrity of oil and gas pipelines. Various assessment models have been developed and used within pipeline industry to predict the burst capacity for pipelines containing longitudinally-oriented surface cracks. These models have different level of conservatism, accuracy, and precision which significantly impacts pipeline operators’ integrity mitigation decisions such as pressure restriction, excavation, and repair, and also lead to different level of safety. This paper compares the accuracy and precision of the most commonly used crack assessment models, i.e. Modified Ln-Sec, CorLAS, API 579 Level 2 and the recent-published PRCI MAT-8 model using in-service and hydrostatic testing failure data. A total number of 12 in-service and 63 hydrostatic test pipe ruptures due to stress corrosion cracking (SCC) with actual burst pressure, material property, and detailed crack size measurements are collected, and used to derive the probabilistic characteristics of the model errors associated with each model. Compared to the burst tests conducted in the laboratory and investigated in other previous studies, the results obtained from in-service and hydrostatic test ruptures are more representative of the real boundary conditions in pipeline operation. All the assumptions and empirical correlations associated with each model are discussed in details. The analysis result suggests that CorLAS is the most accurate model with the least uncertainty (or highest precision). Mitigation activities can be optimized without compromising safety by using the most accurate and precise model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".