Prediction of DSI Parameters from Conventional Well Log Data Using Intelligent System and Clustering tool

Compressional and Shear velocity are two fundamental parameters which have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using Dipole Sonic Imaging tool (DSI), but unfortunately this tool is run just in few wells of a field. Therefore it is important to predict compressional and shear velocity indirectly from the other conventional well logs that have good correlation with these parameters in given well without these logs. The overriding tool of this work is intelligent systems including Artificial Neural Network, Fuzzy Logic and clustering tool Multi-resolution graph-based clustering (MRGC) for prediction of Compressional and Shear velocity. In this paper 1328 data points from one formation which have Compressional and Shear velocity are used. These data are divided into two groups: 998 data points for construction of intelligent systems, and 330 data points used for model testing. The results showed that despite difference in concept, all of the intelligent techniques were successful for estimation of Compressional and Shear velocity but clustering tool is better than other method

Prediction of DSI Parameters from Conventional Well Log Data Using Intelligent System and Clustering tool Keywords:

Compressional velocity , Shear velocity , Dipole sonic imaging , Neural network , Fuzzy logic , Multi-resolution graph-based clustering (MRGC) , Mean square error (MSE