Development of an expert system for assessing failures in oil and gas pipelines due to microbiologically influenced corrosion (MIC)
Bibliographic record
Abstract
This study highlights the modeling of an expert system for the classification of internal microbiologically influenced corrosion (MIC) failures related to pipelines in the upstream oil and gas industry. The model is based on artificial neural networks (ANNs) and involves the participation of 15 MIC experts. Each expert evaluated a number of model case studies ranging from MIC- to non-MIC-driven upstream pipeline failures. The model accounts for variations in microbiological testing methods, microbiological sample types, and degradation morphology, among other variables. It also accounts for missing datasets, which is commonly the case in actual failure assessments. The outcome is an expert system model whose outputs are classes (classification ANN) which comprises MIC potential and data confidence. The performances of two approaches are contrasted in this study. One classifies the output in 5 classes, a 5-output classification (5OC) model; the other in 3 classes, a 3-output classification (3OC) model. The 5OC model had an accuracy of 62.0% while the simpler 3OC model had a better accuracy of 74.8%. This modelling exercise has demonstrated that knowledge from experts can be captured in a reasonably effective model to screen for possible MIC failures. It is hoped that this study not only may contribute to a better understanding of the prevalence of MIC in the oil and gas sector, but also may highlight the key areas necessary to improve the diagnosis of MIC failures in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".