The support vector machine and its application to hydrocarbon discriminant in oil and gas exploration

The methods based on empirical risk minimization are often applied to hydrocarbon discriminant in oil and gas exploration. But the predictive validities of these methods are not perfect with small sample data. This paper introduces a nonlinear support vector machine (SVM) based on structural risk minimization which can obtain global optimization other than local one and better generalization. The nonlinear SVM is with robust predictive performance, especially in small samples. The experimental results in small data show that the nonlinear SVM is robust and may obtain higher recognition rates. Further, the method proposed is effective in hydrocarbon detection or discriminant in reservoir prediction of carbonate rocks.