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- W2391565362 abstract "Reservoir identification plays an important role in oil and natural gas exploration and development.However,the accuracy of conventional identification methods is low.So this paper put forward a PCA-SVM model with integrated Principle Component Analysis and Support Vector Machine to solve this problem.PCA-SVM model not only solve the problems of small-scale sample,high-dimension and easy to fall into local optimal in nonlinear prediction,but also overcome multi-linear correlation among variables and reduced the dimensions of input variables at the same time.Applying the PCA-SVM model to M51 reservoir in Central Gas-field,the result indicated that the accuracy of the PCA-SVM model reached 100%,compared with 93.6% and 96.3% of SVM model and Fisher discriminant analysis model respectively.Therefore,the PCA-SVM model has higher prediction accuracy in reservoir identification,and provide a new method for reservoir identification at the same time." @default.
- W2391565362 created "2016-06-24" @default.
- W2391565362 creator A5054408889 @default.
- W2391565362 date "2010-01-01" @default.
- W2391565362 modified "2023-09-24" @default.
- W2391565362 title "A PCA-SVM MODEL FOR RESERVOIR IDENTIFICATION" @default.
- W2391565362 hasPublicationYear "2010" @default.
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