Integration of fuzzy systems and genetic algorithm in permeability prediction

Abstract

Determine an appropriate distribution of reservoir parameters is a challenge in reservoir engineering. Permeability is one of special reservoir parameters which its modeling is more complicated because there is no direct tool to determine permeability distribution. This problem is more critical in carbonate reservoir because of the fracture effects on measurements. The most reliable way of permeability calculation is laboratory analysis of cores, but this method could not provide a thorough permeability profile in the desired field. In recent years, different methods and algorithms used to predict permeability. One of the most common methods is artificial intelligent methods such as ANN, FL and GA. This paper provides a way to compare the ability of different fuzzy methods to predict permeability from well logs in one of southern Iranian carbonate reservoirs. Sugeno type fuzzy inference system (SFIS), adaptive neuro-fuzzy inference system (ANFIS) and locally linear neuro-fuzzy (LLNF) used to predict permeability. One third of all data used for test the fuzzy systems. Mean square error (MSE) and correlation coefficient (CC) of the test dataset used to select the best method in permeability determination. In final step genetic algorithm is applied to combine different method results to obtain a final model. This algorithm minimizes an error function. This function consists of SFIS, ANFIS and LLNF model predictions. The ability of different methods are compared to find an appropriate method for permeability prediction. © 2013 Springer-Verlag Berlin Heidelberg.