Artificial Intelligence for prediction of porosity from Seismic Attributes: Case study in the Persian Gulf

Porosity is one of the key parameters associated with oil reservoirs. determination of this petrophysical parameter is an essentialstep in reservoir characterization. among different linear and nonlinear prediction tools such as multi-regression and polynomialcurve fitting, artificial neural network has gained the attention of researchers over the past years. in the present study, twodimensional(2d) seismic and well logs data of the burgan oil field were used for prediction of the reservoir porosity. in this regard,broad-band acoustic impedance was first extracted from 2d seismic dataset, as the attribute most related to porosity. next, otheroptimum seismic attributes were selected using stepwise regression and cross validation techniques. at the end, three types of neuralnetwork were used for inversion of seismic attributes and prediction of reservoir porosity. the results show that probabilistic neuralnetwork (pnn) is the best one for prediction of the reservoir porosity using seismic attributes.