基于OGM(1,N)模型的油气注水管道腐蚀速率预测方法  

Corrosion Rate Prediction Method for Oil and Gas Injection Pipelines Based on OGM(1,N)Model

作　　者：李大鹏 孙首群[1] LI Dapeng;SUN Shouqun(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093)

机构地区：[1]上海理工大学机械工程学院,上海200093

出　　处：《计算机与数字工程》2024年第8期2505-2509,共5页Computer & Digital Engineering

基　　金：国家科技支撑计划项目(编号:2015BAK16B04)资助。

摘　　要：腐蚀是油气注水管道失效的主要方式,提高油气注水管道内腐蚀的预测精度是预防管道提前失效、减小造成企业经济损失风险和人员伤亡的重要手段。在原有的灰色理论基础上,论文引入了OGM(1,N)模型对内腐蚀数据进行建模和预测,相比较传统灰色预测模型,该模型的预测精度提升非常明显。为了进一步减小误差,采用自适应粒子群算法优化模型中背景值的选取以及结合代谢数组改进OGM(1,N)模型。通过实验数据验证发现,改进OGM(1,N)模型相比较优化前预测精度再次提高24.9%。Corrosion is the main mode of failure of oil and gas injection pipelines.Improving the prediction accuracy of internal corrosion of oil and gas injection pipelines is an important means to prevent premature failure of pipelines and reduce the risk of economic loss and casualties to enterprises.Based on the original grey theory,this paper introduces the OGM(1,N)model to predict the internal corrosion data,which improves the prediction accuracy significantly compared to the traditional grey prediction model.In order to further reduce the error,the adaptive particle swarm algorithm is used to optimise the selection of background values in the model and to improve the OGM(1,N)model by combining with metabolic arrays.The improved OGM(1,N)model is validated by experimental data and the prediction accuracy is again improved by 24.9%compared to the pre-optimisation model.

关 键 词：管道腐蚀 腐蚀速率 灰色预测 OGM(1 N) 自适应粒子群优化 新陈代谢 

分 类 号：TE832[石油与天然气工程—油气储运工程]