Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS

TL;DR

This paper introduces a hybrid system combining SVM, ANFIS, and domain knowledge to improve oil saturation prediction accuracy from well logs, demonstrating enhanced reservoir characterization performance.

Contribution

The paper presents a novel two-stage DKFIS framework integrating SVM, ANFIS, and expert knowledge for more accurate oil saturation prediction from noisy data.

Findings

DKFIS outperforms standalone ANFIS in prediction accuracy.

Expert knowledge refinement improves feasible and realistic predictions.

Performance metrics confirm DKFIS's effectiveness for reservoir characterization.

Abstract

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.