Wet Gas Pipeline Internal General Corrosion Prediction Based on Improved De Waard 95 Model
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 14, Issue 4
Abstract
Corrosion perforation of wet gas pipelines occurs frequently, threatening the safe production on gas fields and bringing huge economic losses. Therefore, wet gas pipelines need to use a suitable corrosion prediction model to predict the corrosion rate along the pipeline and anticipate the corrosion risk. However, current corrosion prediction models have problems such as large errors and inaccurate predictions. This paper compares four common corrosion prediction models and combines these four models with OLGA multiphase flow simulation calculations to predict the corrosion condition of the same pipeline under three different conditions. Finally, it compares the model predictions with the actual corrosion internal detection data. The predicted value of the De Waard 95 model was closer to the corrosion internal detection data, but the error result still reached 82.03%. To further improve the accuracy of the corrosion prediction model for wet gas pipelines, the De Waard 95 model was optimized using the linear fitting method to obtain a new corrosion prediction model, W22. And the corrosion prediction experiment was conducted for the multiphase flow corrosion loop using the W22 model, and the error between the prediction results and the experimental results was only 6.8%. Compared with the De Waard 95 model, the average error of the W22 model was reduced by 81.32%. Therefore, the W22 model has high accuracy in predicting wet gas pipeline corrosion, which lays the foundation for improving the essential safety of wet gas pipeline operation.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (52174062).
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© 2023 American Society of Civil Engineers.
History
Received: Jan 19, 2023
Accepted: May 25, 2023
Published online: Sep 13, 2023
Published in print: Nov 1, 2023
Discussion open until: Feb 13, 2024
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