  This paper presents the development of a hybrid learning system based on
Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS)
and domain knowledge to solve prediction problem. The proposed two-stage Domain
Knowledge based Fuzzy Information System (DKFIS) improves the prediction
accuracy attained by ANFIS alone. The proposed framework has been implemented
on a noisy and incomplete dataset acquired from a hydrocarbon field located at
western part of India. Here, oil saturation has been predicted from four
different well logs i.e. gamma ray, resistivity, density, and clay volume. In
the first stage, depending on zero or near zero and non-zero oil saturation
levels the input vector is classified into two classes (Class 0 and Class 1)
using SVM. The classification results have been further fine-tuned applying
expert knowledge based on the relationship among predictor variables i.e. well
logs and target variable - oil saturation. Second, an ANFIS is designed to
predict non-zero (Class 1) oil saturation values from predictor logs. The
predicted output has been further refined based on expert knowledge. It is
apparent from the experimental results that the expert intervention with
qualitative judgment at each stage has rendered the prediction into the
feasible and realistic ranges. The performance analysis of the prediction in
terms of four performance metrics such as correlation coefficient (CC), root
mean square error (RMSE), and absolute error mean (AEM), scatter index (SI) has
established DKFIS as a useful tool for reservoir characterization.
