Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran)

Compressional, shear and Stoneley wave velocities (V-p, V-s and V-st, respectively) are important reservoir characteristics that have many applications in petrophysical, geophysical and geomechanical studies. In this study V-p, V-s and V-st were predicted from well log data using genetic algorithms, fuzzy logic and neuro-fuzzy techniques in an Iranian carbonate reservoir (Sarvak Formation). A total of 3030 data points from the Sarvak carbonate reservoir which have V-p, V-s, V-st and conventional well log data were used. These data were divided into two groups; one group included 2047 data points used for constructing intelligent models, and the other included 983 data points used for models testing. The measured mean squared errors (MSEs) of predicted V-p in the test data, using genetic algorithms, fuzzy logic and neuro-fuzzy techniques, were 0.0296, 0.0148 and 0.029, respectively, for V-s these errors were 0.0153, 0.0084 and 0.0184, respectively, and for V-st they were 0.00035, 0.00020 and 0.00062, respectively. Despite different concepts in these intelligent techniques, the results (especially those from fuzzy logic) seem to be reliable. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.