Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data and artificial neural networks

3D seismic data interpretation plays a key role in identifying Lithofacies and their lateral changes for hydrocarbon reservoirs exploration. Among mathematical analysis techniques, Artificial Neural Network (ANN) offers superior handling over inherent non-linearity of seismic data. Here we applied multi-attribute analysis based on ANN methods and well logs data to determine the lithofacies alteration and heterogeneity in one of the structural-stratigraphic oil fields at Persian Gulf. Statistical analysis on seismic attributes together with their geological significance were the main criteria to choose proper seismic attributes for classification. The results showed areas of the shaly- and sandy-dominated facies in the reservoir interval. We suggested further attempts to locate oil reserves at the northeast and southwest parts of the area according to our findings on dominancy of sandy-dominated facies with shaly interlayers in those regions.