基于ASO-BP神经网络的海底油气管道腐蚀速率预测  

Prediction of corrosion rate of submarine oil and gas pipelines based on ASO-BP neural network

作　　者：肖荣鸽 王栋 王勤学 XIAO Rongge;WANG Dong;WANG Qinxue(Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil&Gas Reservoirs,College of Petroleum Engineering,Xi’an Shiyou University,Xi’an 710065,China)

机构地区：[1]西安石油大学陕西省油气田特种增产技术重点实验室,西安石油大学石油工程学院,西安710065

出　　处：《化学工业与工程》2022年第6期109-116,共8页Chemical Industry and Engineering

基　　金：陕西省教育厅服务地方专项计划项目(19JC034);西安石油大学研究生创新与实践能力培养计划资助(21212090)。

摘　　要：随着我国海洋油气管网的发展与建设,管道数据采集量随之增大,优秀的预测模型可以应对大量数据,准确预测管道腐蚀速率,对保障管道安全健康运行具有重大意义。将原子搜索优化算法(ASO)思想引入BP(Back propagation)神经网络,构建ASO-BP神经网络用于海底油气管道腐蚀速率的预测。以50组现场数据为例,使用Matlab进行模拟仿真计算,分别构建具有代表性的BP、GA-BP和ACO-BP模型作为对比,对海底油气管道腐蚀速率数据进行训练和预测,结果表明ASO-BP模型预测精度较高,其平均绝对百分比误差(MAPE)为3.16%,预测结果优于BP、GA-BP和ACO-BP,验证了其可靠性以及良好的预测性能,为海底管道腐蚀速率预测研究提供了新的方法和思路。With the development and construction of offshore oil and gas pipeline network, the amount of pipeline data collection has increased accordingly. Excellent prediction models can cope with a large amount of data and accurately predict the corrosion rate of pipelines, which is of great significance to ensure the safe and healthy operation of pipelines. The idea of atomic search optimization algorithm(ASO) is introduced into BP(Back propagation) neural network, and ASO-BP neural network is constructed to predict the corrosion rate of submarine oil and gas pipelines. Taking 50 sets of field data as an example, Matlab was used for simulation calculations, and representative BP, GA-BP, and ACO-BP models were constructed for comparison. The corrosion rate data of submarine oil and gas pipelines were trained and predicted. The results show that the prediction accuracy of the ASO-BP model is high, the mean absolute percentage error(MAPE) of the model is 3.16%, and the prediction results are better than those of BP, GA-BP and ACO-BP, its reliability and nice prediction performance are verified, which provides a new method and idea for the prediction of submarine pipeline corrosion rate.

关 键 词：海底油气管道 腐蚀速率 原子搜索优化算法 BP神经网络 预测精度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]