Support Vector Machines for Classification and Mapping of Reservoir Data

Abstract

Support vector machines (SVM) is a new machine learning approach based on statistical learning theory (Vapnik-Chervonenkis theory). VC theory has a solid mathematical background for dependencies estimation and predictive learning from finite data sets. SVM is based on the structural risk minimisation principle, aiming to minimise both the empirical risk and the complexity of the model, thereby providing high generalisation abilities. SVM provides non-linear classification and regression by mapping the input space into a higher-dimensional feature space using kernel functions, where the optimal solutions are constructed. This paper presents a review on the use of SVM for the analysis and modelling of spatially distributed information. The methodology developed here combines the power of SVM with well known geostatistical approaches such as exploratory data analysis and exploratory variography. A case study (classification and regression) based on reservoir data with 294 vertically averaged porosity values and 2D seismic velocity and amplitude is presented. Such results are also compared with geostatistical models.