Artificial Neural Networks and Seismic Attributes Applications in the studies of Hydrocarbon Reservoirs _ the Case Study of Hendijan Oil Field

The importance and determinative role of petro-physical properties in the study of oil and gas reservoirs, necessitates that any kinds of information can be used to infer these properties. In this study, the seismic data related to the Hendijan oil field were used along with the available logs of 7 wells of this field in order to use the extracted relationships between seismic attributes and the values of the shale volume in wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (modelbased,bandlimited and sparse spike inversion methods) were applied on it that among this methods, the model-based method presented better results. Then, by using seismic attributes and artificial neural networks, the shale volume was estimated using the three types of neural networks including PNN, MLFN and RBF that the PNN neural network has good results compared to the RBF and MLFN networks.