High Level Corrosion Risk Assessment Methodology for Oil & Gas Systems
Bibliographic record
Abstract
Abstract Assessing corrosion risks and developing appropriate inspection and mitigation measures forms a vital part of Asset Integrity Management (AIM) for operating any ageing asset. However, for many systems detailed information is often scarce and/or unreliable, which prevents or limits the application of many Risk Based Inspection (RBI) databases / software systems, which are “data hungry”. In order to overcome this limitation, and to allow corrosion risk assessment of both existing and aging facilities, an alternative in-house expert system methodology has been developed. The system is designed to accept a range of data inputs including “engineering judgment”, summary of inspection data, monitoring data, predicted corrosion rates, etc as may be available; thereby overcoming problems with sparse / non-existent data, whilst still providing a logical, transparent and fully auditable system for later review, update and modification as may be necessary. The system can be used to drive the development of corrosion monitoring, fluid sampling and inspection plans for process plant. The use of the combined corrosion risk assessment methodology and automated Inspection Plan development, avoids the need for labor intensive inspection driven integrity management systems where data or resources are not available. The overall system is described together with examples of application to both ageing and new facilities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".