Probabilistic neural network approach for porosity prediction in Balkassar area: a case study

Abstract

This study is intended to build a stratigraphic architecture through demarcation of potentially prospective zones through porosity prediction using the Artificial Neural Network. Artificial Neural Network has gained a considerable amount of attention over the past few years among different linear and nonlinear prediction tools such as curve fitting. The current study predicts the reservoir porosity using 3D seismic data and well logs of the Balkassar Oil field. Therefore, to obtain acoustic impedance volume, the 3D seismic data is inverted and applied to the data set by using as a part of seismic attribute study. The stepwise regression and validation testing is found to provide the best results for seven attributes which are used for training the Neural Network, which showed a substantial amount of correlation. On this basis, porosity volumes are predicted. These volumes are used to define zones that could describe the distribution of porosity in the Balkassar Oil field and could be helpful in determining prospective zones. Otherwise it would not be promising by 3D seismic amplitude data. In this way, contemporary research has important implications for future exploration.