Geostatistical seismic inversion constrained by reservoir sedimentary structural features

Abstract

Geostatistical inversion is a technique widely used in seismic inversion. It builds the posterior distribution by incorporating prior information. Abundant prior information for geostatistical inversion is provided by the spatial variability of well log represented by variograms. However, a variogram can only describe the spatial variability of lithologic parameters at a single point. Describing the structural relationship of rock deposition in the vertical direction (the sedimentary rhythm) requires a continuous curve, rather than one single point. We have developed a geostatistical seismic inversion method constrained by sedimentary structural features to enhance the consistency between inversion results with the sedimentary features of a reservoir. This method first uses sparse representation to learn the sedimentary structural features of a reservoir. Then, the structural features are taken as prior information of geostatistical inversion to improve the accuracy of inversion results. The novelty of our method is that the element describing the spatial variability of model parameters is extended from a single point to a sedimentary structural block. We derive an analytical expression of posterior distribution of model parameters with constraints of sedimentary structural features and realize a data-driven geostatistical seismic inversion. Experiments on synthetic and field data indicate promising results consistent with the sedimentary features of reservoir.