A facies-constrained geostatistical seismic inversion method based on multi-scale sparse representation

Abstract

Geostatistical seismic inversion is an important method for establishing high-resolution reservoir parameter models. There is no accurate representation method for reservoir structural features, and prior information about structural features cannot be incorporated into geostatistical inversion. Based on the assumption of the sparsity of stratigraphic sedimentary features, the same type of structural feature is used to represent the sedimentary pattern of reservoirs within the same facies. Different sparse representation patterns are used to represent the differences in sedimentary patterns between facies. Although changes in depositional environment might result in the multi-scale characteristics of geological structures for varying sedimentary rhythms, this paper proposes a facies-constrained geostatistical inversion method based on multi-scale sparse representation to better accommodate such situation. Using the method of sparse representation combined with wavelet transform, the multi-scale sedimentary structural features of reservoirs are learned from well-logging data. Seismic facies and multi-scale features are used as prior information for geostatistical inversion. Further, the likelihood function is constructed using seismic data to obtain the posterior probability distribution of reservoir parameters. Finally, the accurate inversion result is obtained by using multi-scale sparse representation as a constraint in the posterior probability distribution of reservoir parameters. Compared with conventional geostatistical methods, this algorithm can better match the structural features of reservoir parameters with varying geological conditions. Field data tests have shown the effectiveness of this method in improving the accuracy and resolution of reservoir parameter structural features.