Feature Selection,Genetic Algorithm and Support Vector Machine
Hongqi Li, Meng Zhao-xu, Tan Fengqi · Journal of Oil and Gas Technology · 2008
The support vector machine (SVM) was proved effective in identifying the water-flooded oil reservoirs but its prediction performance was sensitive to various factors. A new identification method was presented, where feature subsets were automatically selected by using Relief-F; model parameters were optimized by using genetic algorithms; and the classification accuracy of the small size sample was improved through weighted SVM. The method was applied in the evaluation of water-flooded interval for Lower Karamay reservoir of Liuzhong Area in the Karamay Oilfield. The results show that the SVM model with high generalization performance is obtained and hence the accuracy of the identification of water-flooded interval is effectively improved.