Model for Predicting Corrosion in Steel Pipelines for Underground Gas Storage

The response surface methodology (RSM) is utilized to construct a corrosion prediction model for steel pipelines for underground gas storage (UGS). Four key corrosion-influencing factors—the CO2 partial pressure, Cl− concentration, temperature, and flow rate—are identified by investigating the operating parameters of 14 UGS extracted pipelines (Nos. S1–S14) in China. Based on the operating parameters, 29 sets of high-temperature and high-pressure autoclave corrosion tests are designed and carried out. A quadratic regression equation model for corrosion rate prediction is fitted using the data from the corrosion test results. The p-values of the model’s four influencing factors are <0.01, indicating that the influencing factors are significant and reasonable. The F-value of the model is greater than the critical value, and the noise probability p-value is <0.01, indicating that the model has good fitness. The determination coefficient R2 of the model is 0.9753, which is close to 1. Therefore, the observed value and the response value of the model are obviously correlated: i.e., the model has a high degree of truth. The model is used to predict the corrosion rate of 14 UGS pipelines: S3 and S14 are severely corroded, while the others are moderately corroded.