A Bayesian network‐based susceptibility assessment model for oil and gas pipelines suffering under‐deposit corrosion
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Oil and gas pipelines are exposed to harsh operating conditions that facilitate their susceptibility to complex corrosion mechanisms. This affects their integrity and results in failure with associated consequences. Capturing these complex corrosion phenomena requires a robust approach. This study proposes the application of a dynamic probabilistic model to capture the key influential factors that contribute to the complex under‐deposit corrosion (UDC) mechanism in oil and gas pipelines. The Bayesian network model assesses the pipeline's susceptibility (degradation rate) to the UDC, capturing parametric dependencies. The predicted corrosion rates are input data for the corrosion propagation prediction. Three semi‐empirical corrosion propagation models are used for a comparative assessment to establish the degree of susceptibility given the prevalent influential factors and model parameters. The proposed approach is tested on an offshore pipeline, and the degree of impact of the key influential parameters is predicted. The result shows a percentage increase in the degradation rate by 18.7%, 33.2%, 35.8%, and 63.4%, respectively, for the various interaction scenarios. The present approach offers an adaptive and robust technique that would provide an early warning guide on the rate of pipeline degradation to aid integrity management for offshore assets suffering from deposit corrosion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it