**FUZZY PARTITIONING SYSTEMS FOR ELECTROFACIES CLASSIFICATION:
A CASE STUDY FROM THE MARACAIBO BASIN**

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**J. J. Finol* ^{+}, Y. K. Guo** and X. D. Jing***

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******Centre for Petroleum Studies, T.H. Huxley School of Environment,
Earth Sciences and Engineering, Imperial College, London, SW7 2BP. *

***Fujitsu Parallel Computing Centre, Department of Computing,
Imperial College, London, SW7 2BZ.*

^{+}Author for correspondence: *jose.finol@ic.ac.uk*

This paper describes a method of advanced data processing for
the inverse problem of lithofacies prediction from well logs using fuzzy partitioning
systems. A fuzzy partitioning system consists of a set of fuzzy If-Then rules
of the form "**If** bulk density (*ρ _{b}*)

The aim is to find a minimum set of fuzzy classification rules that can correctly classify all log training patterns. Unlike traditional methods of predicting lithofacies, this approach does not require prior knowledge about the partitioning of the well log readings or any assumption of the facies probability densities. Computer simulations using selected well log responses and facies description from a clastic and carbonate sequence in the Maracaibo Basin (western Venezuela) examine the performance of the fuzzy rule-based classification approach. The performance of the fuzzy classification method is evaluated against the facies classification results using conventional statistical analysis.