Single loop inversion of facies from seismic data using sequential simulations and probability perturbation method

Abstract

The main objective of this work is to present a new methodology for seismic reservoir characterization that provides fine-scaled reservoir models of facies and reservoir properties, such as porosity, net-to-gross, and, possibly, fluid saturation. The proposed iterative methodology is based on sequential simulations of discrete variables, namely sequential indicator simulation, and a stochastic optimization technique called probability perturbation method. At each step of the optimization we generate a facies model, distribute reservoir properties, calculate the corresponding elastic attributes through a rock physics model, compute synthetic seismograms and, finally, compare these synthetic results with the real seismic amplitudes. The stochastic optimization technique perturbs the probability distribution used to generate the initial model and obtains the most probable facies model through a relatively small number of iterations. The method is applied to a real well profile, where three facies have been identified, and finally extended to a real 2D seismic section.