Probabilistic failure assessment of oil pipelines due to internal corrosion
Bibliographic record
Abstract
Abstract Oil and gas pipelines play a key role in the safe and efficient delivery of energy resources around the world. Crude oil by itself is not corrosive, but oil extracted from geological reservoirs is accompanied by varying amounts of water and acidic gases such as carbon dioxide (CO 2 ), which can form a corrosive combination. Estimating the corrosion rate and depth in pipelines is essential for predicting their failure probability. In the present study, a Bayesian network has been developed for predicting the distribution of corrosion rate in oil pipelines given the point estimates generated using an empirical corrosion simulation model. For this purpose, the simulation model considers corrosion parameters such as pipe diameter, flow temperature, flow velocity, and CO 2 partial pressure, among others. With the corrosion rate distribution predicted by the Bayesian network, corrosion depth–rate relationships have been employed to convert the corrosion rate distribution into failure probability distribution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".