Probabilistic seismic petrophysical inversion with statistical double-porosity Biot-Rayleigh model

Abstract

Petrophysical inversion quantitatively extracts reservoir properties from seismic data/attributes, which has gained increasing attention in assisting hydrocarbon reservoir exploration and evaluation. However, given complex reservoirs, its application is limited either due to the adopted rock-physics model or due to the computational demand. We have developed a semianalytic seismic petrophysical inversion method intended for complex reservoirs. The method incorporates the double-porosity Biot-Rayleigh (BR) model into the petrophysical forward operator, to take into account the heterogeneities within reservoir rocks. The inversion process combines statistical rock-physics modeling and Bayesian Gaussian mixture estimation, by which the analytical expression of the results can be achieved with limited computational cost. To better describe pore structure complexities, the inclusion content that constitutes a double-porosity medium of the BR model is considered an unknown variable, which favors the spatial variation of pore structures under complex lithofacies conditions. In addition, the method uses simulated annealing to estimate the inclusion content at a well location to obtain its prior information and takes advantage of the Monte Carlo simulation in rock-physics modeling to stabilize the inversion results. The method is tested and applied to the field data from a carbonate reservoir in southern China, the results of which show that the accuracy of the petrophysical parameters in terms of root-mean-square error are increased by as much as 12% compared with the method based on the Gassmann model.