Application Of SVM In Predicated Reservoir Parameter Form Well Log

Recently, Support Vector Machines (for short SVM) attract many researchers and become a very active field because of its many good properties. SVM is a new and promising technique for classification and regression and have shown great potential in numerous machine learning and pattern recognition problems. This paper discusses the theory of SVM thoroughly, especially how choose the parameter of the Gauss kernel SVM, at last we discusses the application of SVM in predicting reservoir parameter form well log.In the paper, we start with an overview of Statistical learning Theory which is the theoretical foundation of SVM, including the consistency of the study process, and how to control generalization of SVM. We then describe linear Support Vector Machine for separable data, which is to construct the maximal margin separating hyperplane. We explain how to introduce a nonlinear map which maps the input vectors into a feature space. In this space construct an optimal separating hyperplane using the same method, and in fact we have constructed a nonlinear decision function in the input space. We discuss the regression problem in tail at same time. The solution to SVM is a convex quadratic programmes problem at end, and it has a global optimization solution. We will briefly review some of the most common approaches before describing in detail one particular algorithm, Sequential Minimal Optimisation and then implementation it in Matlab by ourselves. The good results of many experiments show that SVM really has great generalization ability. We then focus on Gauss kernel SVM and discuss how the parameterσinfluences the quality of SVM in tail. We also show that Gauss kernel function can describe the likeness degree of the sample. In addition, we propose a new algorithm for finding a good parameterσ, we called inflexion method. What's more, we point out the influence of standardize to predict, and then give mostly scope of the excellent parameterσ, which in Gauss kernel function after standardized.Finally according to actual problem that in petroleum exploration and production field. We apply SVM in predicate reservoir parameter: Porosity, Permeability, from well log. Comparing this method with BP network shows that this new method can avoid the problem of the local optimal solution of BP network, and achieved the effects with higher precision. It is as an exciting method that using SVM in petroleum exploration from a few wells.