Improvements on De Waard-Milliams Corrosion Prediction and Applications to Corrosion Management
Bibliographic record
Abstract
Abstract This paper describes corrosion rate prediction models for the main corrosion mechanisms of carbon steel in Exploration and Production service. The models succeed earlier work by De Waard, Milliams, and Lotz. The paper emphasizes that model accuracy is less of an issue than knowledge of the key corrosivity parameters and the quality of the corrosion control system. Models will be described for the following mechanisms: CO2 corrosion, CO2/H2S corrosion, H2S corrosion, organic acid corrosion, oxygen corrosion, and microbiologically-induced corrosion. Application limits will be indicated. A good comparison with high-quality lab data is only possible for the CO2 corrosion mechanism. Computer programs will be described in which the corrosion prediction models are applied for front-end design and facility integrity management. Use of these programs during the lifetime of a facility provides a way of focusing on corrosion control issues and they are therefore essential tools for pro-active corrosion management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".