Long Term Structural Integrity Considerations for Abandoned Pipelines
Bibliographic record
Abstract
There has been increasing interest across the industry to better understand the possible long term risks associated with out of service pipelines. In Canada, the Canadian Energy Pipeline Association (CEPA), Petroleum Technology Alliance of Canada (PTAC), and the National Energy Board (NEB), have undertaken multiple studies to identify and assess the threats related to pipeline abandonment. [1][2][3] The primary hazards typically identified across industry for pipeline abandonment are associated with long term corrosion degradation, potential for creation of water conduits, possible environmental impacts, and potential for pipeline collapse and associated soil subsidence. Unfortunately, little guidance is presently available to the industry for determining remaining structural capacity of a heavily corroded pipeline to establish likelihood, and possible timeline, of collapse, nor for determining possible subsidence magnitudes associated with large diameter transmission lines. This paper presents a technical case study for an assessment approximating the remaining strength of an abandoned pipeline subject to long term corrosion degradation, considering both general metal loss, and randomized pitting and perforation growth. The work presented used a combination of finite element analyses, and existing industry models for determining load bearing capacity of an abandoned pipeline under varying levels of degradation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".