Estimation of Permeability From Wireline Logs in a Middle Eastern Carbonate Reservoir Using Fuzzy Logic

Abstract

Abstract Permeability is one of the most difficult properties to predict, especially in carbonate reservoirs. The most reliable data of permeability, obtained from laboratory measurements on cores, do not provide a continuous profile along the depth of the formation. This paper presents the use of fuzzy logic modeling to estimate permeability from wireline log data in a Middle Eastern carbonate reservoir. In this study, correlation coefficients are used as criteria for checking whether a given wireline log is suitable as an input for fuzzy logic modeling. The coefficients are enhanced if they are evaluated with respect to the logarithm of core-based permeability values of the given well. After training the fuzzy model on a layer in a given well, permeability predictions were made for other layers in the same well. These predictions were in excellent agreement with permeability values obtained from cores. It was also observed that Subtractive Clustering technique gives better predictions of permeability when compared with Grid Partitioning technique. A parametric study was also conducted to see the effect of type and number of membership functions, combination of log input parameters, and data size on predictions of permeability. The possibility of training the fuzzy program on one well and testing it for other wells in the same formation is also explored.