基于PSO优化的天然气井油管腐蚀速率预测模型  

Corrosion Rate Prediction Model for Natural Gas Well Oil Pipes Based on PSO Optimization

作　　者：杨冰 卿勇 余恒 彭李芳馨 桂小景 吴英 Yang Bing;Qing Yong;Yu Heng;Peng Lifangxing;Gui Xiaojing;Wu Ying(Chongqing Gas District,PetroChina Southwest Oil&Gasfield Company,Chongqing 400707,China;Chongqing University of Science and Technology,Chongqing 401331,China)

机构地区：[1]中国石油西南油气田公司重庆气矿,重庆400707 [2]重庆科技大学,重庆401331

出　　处：《广东化工》2024年第22期50-53,共4页Guangdong Chemical Industry

摘　　要：天然气井中油管普遍存在腐蚀破坏问题,给生产安全带来极大隐患。为了评估油管腐蚀情况,本文提出了一种基于主成分分析-粒子群优化-支持向量机回归(PCA-PSO-SVR)的腐蚀速率预测模型。通过主成分分析对评估指标进行降维处理,并利用粒子群算法优化支持向量机的关键参数。实验结果表明,该模型稳定性好、识别精度高,具备良好的预测精度和泛化能力,具有实际应用价值和意义。Corrosion damage is a common issue in the oil pipes of natural gas wells,presenting a significant threat to production safety.This paper introduces a PCA-PSO-SVR model for predicting corrosion rates in natural gas well oil pipes,incorporating Support Vector Regression(SVR),Principal Component Analysis(PCA),and Particle Swarm Optimization(PSO) algorithms.By leveraging Principal Component Analysis to reduce the dimensionality of evaluation indicators and optimizing key parameters of the Support Vector Machine through the Particle Swarm Optimization algorithm,the model is constructed.Experimental findings reveal that the model demonstrates strong stability,high accuracy in identification,exceptional predictive precision,and robust generalization capabilities,showing its practical value and significance.

关 键 词：油管腐蚀 天然气井 主成分分析 粒子群优化 支持向量机 

分 类 号：TG172[金属学及工艺—金属表面处理]