Interpretation and Evaluation of Near Girth Weld, Short Axial Cracks in a Petroleum Pipeline
Bibliographic record
Abstract
A Canadian crude oil pipeline presented a unique cracking mechanism exhibited by short, branched axial cracks located in the vicinity of girth welds. These attributes, among others, translated into added depth sizing complexity for ultrasonic crack in-line inspection (ILI) tools. The scope presented in this paper encompasses results from three crack ILIs carried out between 2011 and 2013. The assessment and mitigation of such atypical cracks required innovative interpretation and evaluation techniques. First, unique ILI analysis approaches and reporting criteria were implemented and validated beyond established design specifications. The goal was to characterize the very short features at girth welds, while understanding and managing sizing limitations associated with conventional ILI analysis methods. This was attained from a laboratory ILI pull-testing program performed on field cut-outs containing cracks of interest, in addition to detailed non-destructive examinations (NDE) completed in field and laboratory settings. Second, customized, depth-independent, likelihood-based evaluation criteria were developed to identify and mitigate cracking with such distinct attributes. The ensuing model was then validated against a comprehensive field NDE program using different sizing techniques (e.g., Phased Array and Multi-angle Shear Wave). This paper highlights the key findings from the analytical, experimental and field studies and describes the novel methodology followed in the assessment of crack-related features reported by ILI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".