This report is dedicated to description of complex reservoir properties prediction method using neural-computing for stochastic seismic inversion results processing. Some case studies are provided.

It is very important for successful reservoir properties prediction using seismic and well log data to take all a priori geological and geophysical data into account correctly. Stochastic seismic inversion results (cubes of pressure and shear velocities, density, impedance) are defined all over the area of survey, fully correspond with seismic wavefield and with a priori geological and geophysical information too. Refined geological model possesses higher vertical resolution (due to reduction of reflections interference) and provide dimensional physical values unlike seismic amplitude. Application of neural-computer technology with integrated prediction stability check mechanism (Jack-Knife), provide reservoir properties prediction quickly and guarantee good quality.

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