This paper describes a methodology for the integration of well logs and a series of grid-based attributes extracted from interpreted seismic data for prediction of porosity distributions. The studied area is located in the southwest Iran. Before finding a relationship between the target logs and predicted logs from 3D seismic data, we have interpreted the 3D seismic data in the studied area. Also we matched and combined well data with seismic for forward modeling and seismic inversion. Inversion produces a full band acoustic impedance model of earth which improves the vertical resolution. Then we have checked other different inversion methods such as spare spike and model based. Since the model based method resulted with a better resolution outcome, therefore we decided to apply model based inversion method in the reservoir level. In the next step we applied a linear and a non linear transforms between a group of seismic attributes and porosity logs. Then we obtained a relationship for estimating of a volume of the porosity at all locations of the seismic volumetric data. Finally we found an improvement in the porosity prediction from linear multi attribute transforms when using neural network methods.


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