Implementation of API 1183 Recommended Practice for Reliability-Based Assessment of Dents in Liquid Pipelines
Bibliographic record
Abstract
Abstract Prior to the publication of API 1183 (Recommended Practice for Assessment and Management of Pipeline Dents) in 2020, there was no industry consensus on one method to evaluate the Fitness for Purpose for dents to be implemented in integrity management programs. Regulations in Canada and the United States regarding the repair of dents are primarily based on depth and interaction with stress risers. API 1183 has put forth specific methodologies for screening and detailed assessment of dents which consider both strain-based and fatigue-based failure mechanisms. Enbridge Liquid Pipelines had previously presented a framework to support systemwide dent assessment with an efficient reliability-based approach. Following the publication of API 1183, this approach has been further modified to comply with the API recommendations for dent assessment. Both the screening and detailed analyses within this framework account for the properties of the pipe, dent, and interacting features, the operating condition and history of the line, restraint condition, and associated uncertainties. These analysis techniques combine inline inspection results and engineering analysis with their uncertainties, providing a means for quantitative assessment of dents. This paper demonstrates the alignment of Enbridge’s dent management framework with API 1183 recommendations, and discusses the modifications made for probabilistic assessment of dents. In the absence of specific guidelines for probabilistic assessment in API 1183, Enbridge relied on relevant publications and industry best practices for considering uncertainties within the probabilistic assessment. This framework has been implemented for systemwide analysis with over 5,000 geometric anomalies assessed to date. From this implementation experience, the challenges with probabilistic analysis and potential areas of further improvement have been identified and discussed in detail in this paper. In particular, the recommendations in API 1183 regarding dent fatigue assessment, and the fatigue life reduction factor due to weld interaction are observed to be overly conservative. Overall, the reliability-based dent management framework following API 1183 recommendations have proven to be effective, but inefficient due to being overly conservative. Efforts have been made to validate, and where possible, to calibrate the techniques through comparison to experimental results, field findings, and historical failures. These efforts have enabled Enbridge to tackle the over-conservatism of the models for certain combinations and ranges of operating parameters through novel techniques, which are described in this paper.
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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 | 0.076 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".