Casing life prediction using Borda and support vector machine methods

• 作 者: Xu Zhiqian Yan Xiangzhen Yang Xiujuan
• 机 构: College of Mechanical and Electronic Engineering,China University of Petroleum,Dongying,Shandong 257
• 出 处: 石油科学（英文版）
• 发表日期: 2010年3期
• 关键词: Support vector machine method Borda method life prediction model failure modes r

Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper.According to the quantitative analysis of the influence degree and occurrence probability of risk factors,the Borda counts for failure modes are obtained with the Borda method.The risk indexes of failure modes are derived from the Borda matrix.Based on the support vector machine(SVM),a casing life prediction model is established.In the prediction model,eight risk indexes are defined as input vectors and casing life is defined as the output vector.The ideal model parameters are determined with the training set from 19 wells with casing failure.The casing life prediction software is developed with the SVM model as a predictor.The residual life of 60 wells with casing failure is predicted with the software,and then compared with the actual casing life.The comparison results show that the casing life prediction software with the SVM model has high accuracy.