Forecast model for gas well productivity based on PSO and SVM

摘要：

　　It is very important to forecast the gas well productivity of gas reservoir accurately.On the basis of analyzing the parameter performance of support vector machine (SVM) for regression estimation, the paper proposes gas well productivity prediction model based on particle swarm optimization (PSO) and SVM.The parameter of SVM was optimized by PSO.This method took advantage of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO.Compared with BP neural network model, the proposed GA-SVM model for gas well productivity in practical engineering has higher accuracy and speed, and the maximum error is 2.8%.Thus, it provided a new approach to predict the gas well productivity.

关键词： gas well productivity PSO SVM forecast model

作者: Min Yang Jun Li Jingcheng Liu

作者单位: Chongqing University of Science and Technology, Chongqing, China,401331 Sinopec Northwest Oilfield Branch Yakela gas plant,Xinjiang, China,842017 Chongqing University of Science and Technology, Chongqing, China,401331 ;Chongqing Petroleum And Nat

会议类型: 国内会议

会议名称: 2011全国特殊气藏开发技术研讨会

会议地点: 重庆

会议语种:英文

页码: 276-281

在线出版日期: 2011-11-01（万方平台首次上网日期，不代表论文的发表时间）