The fast Fourier transform moving average(FFT-MA)method is a flexible and efficient stochastic modeling method,which is of great importance in some aspects such as high-resolution modeling of subsurface media,non-stationary modeling of complex media and uncertainty evaluation.Accurately constructing the spa-tial structure model is the key to generating a reasonable stochastic model by the FFT-MA method.However,in the previous research on the FFT-MA method,no effective method for accurately constructing the spatial structure model has been proposed.Therefore,an effective estimation method for spatial structure model is pro-posed.Based on the idea of inversion,by minimizing the spatial structure difference between the stochastic model and the logging data as well as the seismic data,the vertical autocorrelation length and the lateral autocor-relation length are estimated respectively.In order to optimize the method's estimation performance for the spa-tial structure model,the edge-preserving regularization is introduced in the inversion process of the vertical auto-correlation length to enhance the stability of the inversion.In addition,seismic constraints are introduced into model optimization process to improve the stability of the stochastic model.Experimental results show that this method can stably estimate the non-stationary spatial structure model of underground media and thus helps es-tablish a high-resolution stochastic model that accurately describes the non-stationary spatial correlation charac-teristics of complex reservoirs.Compared with the stochastic modeling method based on sequential Gaussian co-simulation,the FFT-MA stochastic modeling method with an optimized spatial structure model can effectively present various complex geological structures,by which complex reservoir modeling can be achieved.
维普
万方数据