基于CO₂/H₂S共存腐蚀环境的复杂性、危险性，以及两者协同与竞争效应的不确定等原因，套管钢在CO₂/H₂S共存腐蚀环境中腐蚀速率测试存在试验时间长、误差较大且存在不安全隐患等缺陷，现有的单一腐蚀速率预测模型不能满足这方面的研究。利用建立的遗传算法优化BP神经网络模型分别对不同温度、不同CO₂分压和不同H₂S分压条件下套管钢的腐蚀速率进行预测。与单纯的BP神经网络模型预测相比，遗传算法优化BP神经网络训练收敛速率有所增加，预测效果得到改善；遗传算法优化BP神经网络预测值与实测值吻合较好，此预测模型可靠性很强；该方法为我国高酸性气田开发中快速获取腐蚀速率数值提供了一条新的思路。

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.