Authors
Abstract
This study explores the application of Takagi- Sugeno fuzzy inference system to predict reservoir permeability form depth and porosity measurements for Mashrif Formation in Nasyria Oil Field, south of Iraq. The models developed intend to describe the non-linear relationship between depth and porosity as inputs and permeability as output. A total of 206 core samples from three exploration wells (Ns-2, Ns-3, and Ns-5) were used to build a fuzzy model. Input data were divided into two groups including training set (170 data points) which represent the Ns-2 and Ns-3 wells; and testing set (36 data points which represent Ns-5). All membership functions and IF-THEN rules of the inference system were derived by using subtractive clustering technique. The performance of the model was measured by using degree of determination. The results of this study indicate that fuzzy logic technique is suitable to infer permeability from depth and porosity measurements alone without the need for the very expensive coring process. The calculated degree of determination was 0.98 for testing data set. A few core permeability and porosity measurements are required first to build fuzzy model and the fuzzy inference engine predict permeability for other sites of the field by knowing depth and porosity inputs which can be taken from conventional well logs data
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