Prediction of Compressional and Shear Slowness from Conventional Well Log Data: Using Intelligent Systems

Abstract

Compressional slowness (DTCO) and shear slowness (DTSM) are two fundamental parameters that have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using a dipole sonic imaging tool, but unfortunately this tool is run in just a few wells of a field. Therefore, it is important to predict compressional and shear slowness indirectly from the other conventional well logs that have strong correlation with these two parameters when the dipole sonic log is missing in a given well. The overriding impetus of this work is to construct intelligent systems, including artificial neural network, fuzzy logic, and adaptive neuro fuzzy inference system, for prediction of compressional and shear slowness. Finally, the results are combined with a committee machine algorithm using a simple averaging technique. A total number of 2,715 data points from Asmari formation, which has compressional and shear slowness, are used. These data are divided into two groups: 2,172 data points for construction of intelligent systems and 543 data points used for model testing. The results showed that, despite a difference in concept, all of the intelligent techniques were successful for estimation of compressional and shear slowness.