Pragmatic Approach to Estimate Corrosion Rates for Pipelines Subject to Complex Corrosion
Bibliographic record
Abstract
Corrosion is a common degradation process for most oil and gas pipelines in operation that can lead to leak and rupture failures. To avoid failures due to corrosion, integrity management plans for pipelines require fitness-for-service (FFS) assessments and remaining life analysis of the corrosion features that are detected by in-line inspections (ILIs). The objective of the present paper is to support the deterministic integrity and remaining life assessment of pipelines by introducing a pragmatic approach for the determination of corrosion rates from two inspections. The proposed approach is primarily tailored towards upstream and subsea pipelines that are subject to very high density internal corrosion rather than transmission pipelines with low to moderate densities of external features. ILI data may be subject to significant measurement errors and feature matching for two ILIs can become highly unreliable if high-density corrosion is present. To address these uncertainties, the backbone of the proposed approach is to focus on corrosion clusters rather than individual corrosion pits and a filtering process is utilized to identify true corrosion growth. The introduced approach is supported by theoretical knowledge and practical experience. The approach can be easily executed in spreadsheet software tools without the application of advanced statistical and probabilistic methods for the deterministic remaining life assessment in practice.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".