Using Committee Machine with Intelligent Systems for Permeability Prediction, A case study of South Pars Gas Field, Persian Gulf, Iran

摘要

Permeability is the ability of porous rock to transmit fluid. An accurate knowledge is necessary for reservoir management and development. This study presents an improved model based on the integration of petrographic data, conventional logs and intelligent systems to predict permeability. The permeability was first predicted using the individual intelligent systems including a neural network (NN), a fuzzy logic (FL) and a neuro-fuzzy (NF) model. Afterwards, a committee machine with intelligent systems (CMIS) was used in order to combine permeability values calculated from the individual intelligent systems. The CMIS using genetic algorithm (GA) model to obtain the optimal contribution of each expert. The results show that CMIS performed better than NN, FL and NF models acting alone. The proposed methodology was applied to South Pars gas field, which is located in the Persian Gulf between Iran and Qatar.