Probability of Exceedance (POE) Methodology for Developing Integrity Programs Based on Pipeline Operator-Specific Technical and Economic Factors
Bibliographic record
Abstract
With the availability of in-line inspection data, pipeline operators have additional information to develop the technical and economic justification for integrity verification programs (i.e. Fitness-for-Purpose) across an entire pipeline system. The Probability of Exceedance (POE) methodology described herein provides a defensible decision making process for addressing immediate corrosion threats identified through metal loss in-line inspection (ILI) and the use of sub-critical in-line inspection data to develop a long term integrity management program. In addition, this paper describes the process used to develop a Corrosion In-line Inspection POE-based Assessment for one of the systems operated by TransGas Limited (Saskatchewan, Canada). In 2001, TransGas Limited and CC Technologies undertook an integrity verification program of the Loomis to Herbert gas pipeline system to develop an appropriate scope and schedule maintenance activities along this pipeline system. This methodology customizes Probability of Exceedance (POE) results with a deterministic corrosion growth model to determine pipeline specific excavation/repair and re-inspection interval alternatives. Consequently, feature repairs can be scheduled based on severity, operational and financial conditions while maintaining safety as first priority. The merging of deterministic and probabilistic models identified the Loomis to Herbert pipeline system’s worst predicted metal loss depth and the lowest safety factor per each repair/reinspection interval alternative, which when combined with the cost/benefit analysis provided a simplified and safe decision-making process.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".