Application of SVM method in reservoir prediction.
Yue Youxi and Yuan Quanshe. College of Geo-resources and Information, China University of Petroleum, Dongying 257061,China
Most classical learning methods are based on the empirical risk minimization (ERM) rule. Usually, these methods exist an over fitting problem when being used to resolve actual problem. By generalizes the error topside's minimization, the Support Vector Machine (SVM) method namely are nonlinear pattern recognition method and nonlinear function estimation method based on the structure risk minimization can get maximum universality and global optimizatioa Using a sandy body in Shengli Oil field as a research target, waveform datum is taken as input vectors. This method makes use of seismic waveform's characters completely. At the same time, it avoids plenty of works during attributes optimization and abstracting parameters partly. This way can be carried out more conveniently and shows a good result.
【CateGory Index】： P631.4