An Intelligent Identification Method of Fuzzy Models and Its Applications to Inversion of NMR Logging Data

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn this paper we describe a novel approach to fuzzy model identification that gives solution to the inverse problem of permeability prediction from NMR data. The fuzzy logic approach uses fuzzy If-Then rules to establish the relationship between permeability (output variable) and the NMR T 2 distribution mean values φ NMR , φ FF , φ BF (input variables). We introduce an intelligent data-driven method that generates the fuzzy rules in a two-steps learning algorithm. In the first step, fuzzy clustering is performed on a set of input-output core measurements to obtain an initial approximation of the fuzzy rules in a rapid prototyping approach. This set of observations is the only information assumed about the model behavior. In the second step, the antecedent and consequent parameters of the identified fuzzy rules are fine-tuned by means of a gradient descent method. The identified fuzzy model is subsequently used to estimate permeability in uncored wells in the same field.Computer simulations using data from a complex siliclastic sequence in the Maracaibo Basin (western Venezuela) show the advantages of this methodology over the conventional empirical and statistical inversion methods.