Mixed Gaussian stochastic inversion based on hybrid of cuckoo algorithm and Markov chain Monte Carlo

Geostatistical stochastic inversion can obtain higher resolution results than conventional inversion. Considering that there are obvious differences in the parameters of different lithofacies in the underground, in this paper, we propose a new method to inverse the petrographic proportion, lithofacies classification and elastic parameters simultaneously, achieving a detailed description of the reservoir parameter distribution under different lithofacies. When considering the multiparameter simultaneous inversion problem of high-dimensional data, the paper realizes the fusion of the cuckoo algorithm and the Markov chain Monte Carlo approach to solve this inversion problem. Our new algorithm uses multiple Markov chains to carry out Levy flight to generate a new solution strategy to expand the solution spatial range. Selecting the optimal solution through the best fitness to achieve the global optimization iteration. Through algorithm integration, the stability and global optimality of the inversion method are effectively improved. The effectiveness of the new method is verified by synthetic data and the field data.