Estimation of permeability using uncertainty methods: type -2 fuzzy system

Permeability is a property of the reservoir rock, which deals with the flow of fluid from the reservoir and is an important factor in oil and gas production. This parameter is measured via coring and core laboratory analysis, which is an expensive and time-consuming process and also is not a feasible approach for every oil and gas field. Nowadays, the permeability can also be calculated using the data of petrophysical logs by means of statistical and intelligent techniques. The present study uses four wells drilled in Kangan and Dalan formations within South Pars gas field to predict permeability using fuzzy logic. Out of totally eight features extracted from each well, four more effective features were selected using correlation-based feature selection tools. Then, regression, multi-layer perceptron, RBF neural network, Local Linear Model Trees (LOLIMOT), type-1 and type -2 fuzzy systems were utilized for permeability prediction. The results indicated that due to the uncertainty in the petrophysical and permeability parameters, type-2 Fuzzy systems cover better the uncertainties. The aforementioned method predicts the best number of rules using the GSA-GA (Gravitational Search Algorithm - Genetic algorithm) combined algorithms. Fuzzy membership functions were also improved using the K-means clustering algorithms. These improvements led to increased accuracy of the predicted permeability with a coefficient of 0.9768, and a decrease in the root mean square error to 0.1602.