A rule based fuzzy model for the prediction of petrophysical rock parameters

A new approach for the prediction of petrophysical rock parameters based on a rule-based fuzzy model is presented. The rule-based fuzzy model corresponds to the Takagi–Sugeno–Kang method of fuzzy reasoning proposed by Sugeno and his co-authors. This fuzzy model is defined by a set of fuzzy implications with linear consequent parts, each of which establishes a local linear input–output relationship between the variables of the model. In this approach, a fuzzy clustering algorithm is combined with the least-square approximation method to identify the structure and parameters of the fuzzy model from sets of numerical data. To verify the effectiveness of the proposed fuzzy modeling method, two examples are developed using core and electrical log data from three oil wells in Ceuta Field, Lake Maracaibo Basin. The numerical results of the fuzzy modelling method are compared with the results of a conventional linear regression model. It is shown that the fuzzy modeling approach is not only more accurate than the conventional regression approach but also provides some qualitative information about the underlying complexities of the porous system.