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《Progress in Geophysics》 2009-01
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A novel method for well logging lithologic identification

MA Hai1,WANG Yan-jiang1,HU Rui2,WEI Mao-an2(1.College of Information and Control Engineering,China University of Petroleum,DongYing 257061,China; 2.Information Center of Drilling Technology Research Institute ShengLi Oil Field Company,Ltd.,DongYing 257017,China)  
A novel support vector machine based on particle swarm optimization(PSO-SVM) is proposed for better solving the well logging lithologic identification problem.An identification model for well logging lithologic is established using the data of actual well logging and lithologic profile by training the SVM,which is optimized by PSO algorithm.The proposed method is applied to certain well in Junggar Basin and the experimental results show it has higher identification precision,faster convergence speed and better generalization effect than BP neural network based approach.
【Fund】: 中国石油化工股份有限公司重点科技攻关项目(JP04014)资助
【CateGory Index】: P631.81
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