Stochastic inversion combining seismic data, facies properties, and advanced multiple-point geostatistics

We proposed a novel seismic inversion approach that integrates the physical properties of litho-facies, and geophysical data, within a multiple-point geostatistical framework to reduce the uncertainty in predictions of litho-facies spatial arrangement away from wells or control points. The litho-facies groups (rock-type) in the well locations are defined and conditioned to the distribution of elastic properties, including the values of P-wave velocity (Vp) and facies density (rho) within the borehole. A conceptual geological model (training image) is uti-lized within a wavelet-based multiple-point geostatistical simulation (WAVESIM) algorithm to generate litho-facies realizations. In our inversion algorithm, the forward model is created by implementing the bivariate Kernel density estimation (KDE) technique of the litho-facies properties (Vp and rho) that are distributed within the well locations. We utilize an iterative inversion framework, where a particular number of elastic properties (Vp and rho) for each WAVESIM realization are drawn. For each generated set of the WAVESIM realizations, we simulate reflectivity series by KDE that are convolved with the seismic wavelet to create synthetic seismograms. Then, using a normalized, cross-correlation function we select the realization that provides the best-match be-tween synthetic seismogram and the input seismic data. Our inversion technique is successfully applied to synthetic and field datasets. Our results demonstrate the efficiency of our inversion to characterize highly het-erogeneous reservoirs in a reasonable computational time and control the connectivity between the channels.