No abstract

Seismic amplitude variation with offset (AVO) inversion from pre-stack seismic data plays a significant role in estimating the elastic parameters and characterizing reservoir properties. Generally, sparse regularization is widely used to solve ill-posed inverse problems by reducing the solution space of subsurface parameters, which makes seismic AVO inversion more stable. However, the traditional sparse constraint inversion only focuses on the vector sparsity of reflectivity, instead of the structural sparse characteristics of estimated parameters. Consequently, various elastic parameters demonstrate different formation structural features in the same location of stratum. In this study, we propose a novel approach that combines the structural sparsity and the vector sparsity of the model reflectivity to establish the posterior probability density distribution and solve the objective function of model parameters. Based on the relationship among multiple elastic parameters, we divide the model parameters to be inverted into several groups according to intrinsic structural sparse characteristics of elastic parameters. In this case, all model parameters at the same sampling point are classified into the identic group, which ensures that different estimated parameters show the same characteristic in terms of stratigraphic structure. From the perspective of Bayesian inference, we utilize the modified Cauchy Probability Density Function (PDF) to characterize the group sparsity and describe the relationship among model parameters in the same group by Gaussian PDF. Furthermore, we estimate the optimum solution corresponding to the maximum posteriori probability under Bayesian inference. Synthetic experiments on Marmousi model prove that the estimated P-velocity, S-velocity and density are consistent with those of the real models, and the application of field data confirms the availability and feasibility of group sparse inversion.

The presence of coal in complex structures featuring sandstone reservoirs seriously hinders reservoir characterization and the identification of fluids in subsurface formations. Coal can strongly obscure the reflections from sandstone, easily leading to false bright spots during exploration; thus, reservoirs and their boundaries cannot be accurately described. Furthermore, sandstone layers intercalated with thin coal seams form complex composite reflections. Therefore, considering the complexity of coal-bearing reservoirs together with the geological evolution of coal and actual logging data, this study systematically analyses the seismic reflections of coal-bearing reservoirs. First, the seismic responses of various coal-bearing reservoir models are established by evaluating multiple well logs of the target layer. Then, by forward-simulating theoretical seismic data, seismic response models comprising different lithological combinations are established. Finally, seismic attributes (such as the amplitude, frequency and phase) of coal-bearing and non-coal-bearing strata are compared, and the seismic responses of typical lithological combinations of coal-bearing reservoirs are summarized. A single-well model test and a comparison between synthetic and seismic data confirm that numerical simulations can be used to forward model the seismic response characteristics of different sand–coal models, thereby eliminating the influences of coal and accurately characterizing sandstone reservoirs.