Stochastic Inversion with a Global Perturbation Method

Geostatistics has been commonly used in forward modeling and in inverse modeling to integrate seismic information in stochastic fine gride models. The quality of seismic and the downscaling of seismic attributes to the fine grid of the well measurements are still challenges to which existing geostatistical methods only give partial answers.<br>In this paper an iterative inversion methodology is proposed based on a direct sequential simulation and co-simulation approaches. Several images of acoustic impedances of entire field are simulated in a first step. Afterwards, co-simulations are used for the global transformation of images of acoustic impedances in an iterative process: after the convolution, local areas of best fit of the different images are selected and “merged” into a secondary image for the direct co-simulation of the next iteration. The iterative and convergent process continues until a given match with an objective function is reached. Spatial dispersion and patterns of acoustic impedances (histograms and variograms) are reproduced at the final acoustic impedance cube.