A Functional Networks-Type-2 Fuzzy Logic Hybrid Model for the Prediction of Porosity and Permeability of Oil and Gas Reservoirs

A hybrid computational intelligence model, integrating the least-squares fitting algorithm of Functional Networks with Type-2 Fuzzy Logic System, is presented. The hybrid model capitalizes on the capability of the least-squares fitting algorithm to reduce the dimensionality of input data while selecting the dominant variables. The model was evaluated with the prediction of porosity and permeability of oil and gas reservoirs. The model attempts to improve the performance of Type-2 Fuzzy Logic whose complexity is increased and performance degraded with increased dimensionality of input data. The Functional Networks block was used to select the dominant variables from the six core and log datasets. The dimensionally-reduced datasets were then divided into training and testing subsets using the stratified sampling approach. Hence, the Type-2 Fuzzy Logic block is trained and tested with the best and dimensionally-reduced variables from the input data. The results showed that the Functional Networks-Type-2 Fuzzy Logic hybrid model performed better in terms of training and testing with higher correlation coefficients, lower root mean square errors and reduced execution times than the original Type-2 Fuzzy Logic system. The success of this work has confirmed the bright prospect for the implementation of more hybrid models with better performance indices.