Geological model sampling using PCA-assisted support vector machine for reliable channel reservoir characterization

Abstract

History matching is a crucial procedure for predicting reservoir performances and making decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for sampling good geological models using support vector machine (SVM) and principal component analysis (PCA).First, we perform PCA for figuring out main geological characteristics of reservoir models. By the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train a SVM classifier using 10% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (5% for each category). Then, the other 90% models are classified by the trained SVM. We select models on the side of lower WOPR errors. By repeating the classification process, we can select reliable models which have similar geological trend with the true reservoir model.The two channel fields are analyzed to demonstrate the superiority of the proposed sampling scheme. History matching results with 400 initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. In addition, since it uses all the models, it takes long computational time. However, history matching with the sampled ensemble offers reliable characterization results by figuring out proper channel trend and gives dependable prediction of future performances. Furthermore, it increases computational efficiency by using only selected geological models in history matching.