A Model to Predict Internal Pitting Corrosion of Oil and Gas Pipelines
Bibliographic record
Abstract
Abstract A practical model has been developed to predict internal pitting corrosion of oil and gas pipelines. This model, applicable for both sour and sweet production and transmission pipelines, is based on experiments carried out in the laboratory at high pressure and high temperature under the operating conditions of the oil and gas pipelines and on the actual pit growth rates in six operating fields over a period of four years. The inputs required to use the model are readily available from the field. The inputs are of two kinds: construction (pipe diameter, pipe wall thickness, and pipe inclination) and operational (production rates of oil, water, gas, and solid, temperature, total pressure, partial pressures of H2S and CO2, and concentrations of sulphate, bicarbonate, and chloride ions). The model accounts for the statistical nature of the pitting corrosion, predicts the growth of internal corrosion pits based on field operational parameters, considers the variation of the pitting corrosion rate as a function of time, and determines the error in the prediction. The model was validated using integrity management data obtained from an operating pipeline.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".