Validation of Corrosion Models Using Field Data Obtained from Oil and Gas Production Pipelines
Bibliographic record
Abstract
Abstract A model was previously developed to predict internal pitting corrosion of oil and gas pipelines. The model considers the influence of temperature, total pressure, H2S partial pressure, CO2 partial pressure, the concentrations of sulfide, bicarbonate and chloride ions and the production rates of oil, gas, water, and solids on pitting corrosion rate. The model was 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 was validated by field trials and using field data. The model accounts for the statistical nature of pitting corrosion. Recently, a microbiologically influenced corrosion (MIC) risk model was also developed and merged with the internal pitting corrosion model so that the integrated model predicts the optimized pitting corrosion rate due to both non-MIC and MIC activities. Twenty years of field data was collected from 19 pipe sections from two sites (Site A and B) in an oil and gas production field. Within the limitations of field data and model inputs, the model prediction correlated well with the data.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".