Analysis of Corrosion in Pipelines Using Computational Fluid Dynamics and Corrosion Rate Prediction Models
Bibliographic record
Abstract
This study delves into the comprehensive analysis of the impact of various parameters on CO 2 corrosion within the oil and gas industry.The primary focus is directed towards understanding the influence of temperature, pH value, CO 2 partial pressure, supersaturation and the development of corrosion product films on the corrosion rate.The simulation of a two-phase water-CO 2 fluid flow was executed in a horizontal pipe characterized by a length (l) of 5000 mm and a diameter (d) of 127 mm, utilizing the OpenFoam software package.To predict the corrosion rate, a NORSOK M -506 prediction model, implemented in the Python programming language, was employed.Mesh generation was performed by the Salome software package, and post-processing procedures were executed using the Paraview software package.To ensure that the analyzed results were independent of the mesh, a mesh refinement study was conducted using five systematically refined meshes.The simulation results were subsequently utilized as input parameters for the developed NORSOK M -506 prediction model, and the model's accuracy was validated against measurement data.The analysis showed that temperature had the greatest impact on the corrosion rate in the pipeline.Operating temperatures within the range of 100 -50 °C were identified as conducive to the formation of a protective film, effectively decelerating the corrosion rate.In contrast, other parameters such as pH value, CO 2 partial pressure, and fluid flow rate exhibited a comparatively diminished impact on the corrosion rate under the specified conditions.Consequently, the determined annual corrosion rate amounted to 0.5 ± 0.2 mm per year.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".