Journal of Petroleum Geology, Vol.18, No.2, pp. 191-206, 1995

©Copyright 2000 Scientific Press, Ltd.

A CRITICAL COMPARISON OF NEURAL NETWORKS AND DISCRIMINANT ANALYSIS IN LITHOFACIES, POROSITY AND PERMEABILITY PREDICTIONS

P. M. Wong*, F. X. Jian*, and I. J. Taggart**

* Centre for Petroleum Engineering, The University of New South Wales, NSW 2052 Australia.

** Current Address: West Australian Petroleum Pty. Ltd. GPO Box 51580 WA 6001 Perth Australia.

Abstract

The application of a genetic reservoir characterisation concept to the calculation of petrophysical properties requires the prediction of lithofacies followed by the assignment of petrophysical properties according to the specific lithofacies predicted. Common classification methods which fulfil this task include discriminant analysis and back-propagation neural networks. While discriminant analysis is a well-established statistical classification method back-propagation neural networks are relatively new and their performance in predicting lithofacies porosity and permeability when compared to discriminant analysis has not been widely studied. This work compares the performance of these two methods in prediction of reservoir properties by considering log and core data from a shaly glauconitic reservoir.