Application of Genetic Algorithms BP Neural Networks to Predicting Corrosion Rate of Carbon Steel in CO₂/H₂S Envitonment

Due to the complexness, riskiness and uncertainty of coordination and synergic effect of corrosion in CO₂/H₂S environment, the corrosion rate testing of casing steel needs a long time test and shows relatively large error and existence of hidden dangers in CO₂/H₂S environment, and the existing single corrosion rate prediction model cannot meet the demands in the research. The established model of BP artificial neural network optimized by genetic algorithm was used to test the corrosion rates at different temperature, CO₂ pressure and H₂S pressure. Compared to the BP neural network, the BP artificial neural network optimized by genetic algorithm increased the convergence rate of train and improved the effect of forecast, and the values from both prediction and actual measurement of BP artificial neural network optimized by genetic algorithm were in good agreement. This model also had a strong reliability. This method provides us a new way to acquire the figure of the corrosion rates fast in high acidy oil-gas field.