Identification of Oil/Gas and water zones in geological logging with Support-vector Machine

Support Vector Machines (SVM) is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of Structural Risk Minimization (SRM) induction principle, therefore, SVM is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. The theory and method of Support Vector Machines based the Statistical Learning Theory and proposed a pattern recognition method based Support Vector Machine to determine oil, gas and water zones in geological logging are studied. The outline of the method is as follows: First, the basic parameters of the gasometry logging and geochemistry logging, and induced some significant parameters, such as hydrocarbon moistness index, pyrolysis hydrocarbon equilibrium index, gasometry hydrocarbon equilibrium index ,etc. are researched, which can help to distinguish the feature of reservoir; then the Support Vector Machine to study the relationship of those parameters is used, and the recognition mode and develop its program to determine of oil, gas and water zones are set up. Application and analysis of the experimental results in Xinjiang oilfield proved that SVM can achieve greater accuracy than the BP neural network does. It proved that identification of oil/gas and water zones in geological logging with SVM is reliable, adaptable, precise and easy to operate.