Formal reliability analysis of oil and gas pipelines
Bibliographic record
Abstract
Depending on the operational environment, installation location, and aging of oil and gas pipelines, they are subject to various degradation mechanisms, such as cracking, corrosion, leaking, and thinning of the pipeline walls. Failure of oil and gas pipelines due to these degradation mechanisms can lead to catastrophic events, which, in the worst case, may result in the loss of human lives and huge financial losses. Traditionally, paper-and-pencil proof methods and Monte Carlo based computer simulations are used in the reliability analysis of oil and gas pipelines to identify potential threats and thus avoid unwanted failures. However, paper-and-pencil proof methods are prone to human error, especially when dealing with large systems, while simulation techniques primarily involve sampling-based methods, i.e., not all possible scenarios of the given systems are tested, which compromises the accuracy of the results. As an accurate alternative, we propose to use a higher-order-logic theorem proving for the reliability analysis of oil and gas pipelines. In particular, this paper presents the higher-order-logic formalization of commonly used reliability block diagrams (RBDs), such as series, parallel, series–parallel, and k-out-of- n, and provides an approach to utilize these formalized RBDs to assess the reliability of oil and gas pipelines. For illustration, we present a formal reliability analysis of a pipeline transportation subsystem used between the oil terminals at the Port of Gdynia, Poland, and Dębogórze.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".