How Safe Failure Pressure Ratios are Related to %SMYS
Bibliographic record
Abstract
Majority of the pipeline operators manage corrosion using inline inspection (ILI). ILIs enable operators to choose which anomalies should be excavated and, on the flip side, which anomalies are safe to remain on the pipe. If the remaining anomalies grow to a critical size before the next ILI cycle there will be in-service failure. This is avoided by forecasting the growth of the measured defects appropriately and assessing them for future integrity. When an ILI is performed on a pipeline the ILI vendor reports the anomaly sizes, burst pressures and the Failure Pressure Ratios for each anomaly. Failure Pressure Ratio (FPR) for an anomaly is defined as the burst pressure divided by the Maximum allowable operation pressure (MAOP). In order to avoid ruptures, operators will excavate anomalies that have a limiting value of FPR. These limiting values are also referred to as response criteria, excavation criteria, safe failure pressure ratios, or safety factors. As the FPR depends heavily on the operating stress (or %SMYS) the limiting FPR value, below which we respond, should also depend on %SMYS. However, currently there is inconsistency in the use of response criteria in the industry. Some utilize the same response criteria for all pipe irrespective of %SMYS. This study shows that using the same limiting FPR does not provide the same level of safety for different %SMYS scenarios. It shows that the size of remaining anomalies in the lower %SMYS pipe would be significantly larger than in higher %SMYS pipe, leading to lower reliability in pipelines that have lower %SMYS. For gas pipelines, these lower %SMYS pipelines are often in higher location class pipelines with higher consequences of failure. In other words, even though the FPR value is the same for two anomalies in two different %SMYS pipes, the two anomalies would have very different probabilities of failure, where one anomaly could be safe while the other is not. This paper examines the ranges of FPR and the safe response criteria as a function of %SMYS. It examines the remaining anomaly sizes in different %SMYS pipe as allowed by current standards. The effect of the uncertainties on the response criteria due to measurement errors, material and geometric properties and model errors are also examined. It examines the response criteria in different jurisdictions. The effect of using different assessment equations, such as Modified B31G or RSTRENG, on the response criteria is also discussed. In order to obtain consistent safety, the consideration of %SMYS and consequences in defining a response criterion is discussed. Many sets of ILI data, which have pipe in different %SMYS, have been assessed in order to examine practical ranges of FPR with %SMYS. The practical implications of different response criteria are studied and discussed.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".