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 correspo nds 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 implicati ons with linear consequent parts, each of which establishes a local linear input-output relationship between the variables of the model. In this appro ach, a fuzzy clustering algorithm is combined with the least-square approxi mation method to identify the structure and parameters of the fuzzy model f rom sets of numerical data. To verify the effectiveness of the proposed fuz zy modeling method, two examples are developed using core and electrical lo g data from three oil wells in Ceuta Field, Lake Maracaibo Basin. The numer ical results of the fuzzy modelling method are compared with the results of a conventional linens regression model. It is shown that the fuzzy modelin g approach is not only more accurate than the conventional regression appro ach but also provides some qualitative information about the underlying com plexities of the porous system. (C) 2001 Elsevier Science B.V. All rights r eserved.