Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network
This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space,fully considering geological uncertainty and the dynamic characteristics of reservoirs under time-varying well control conditions,creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks(CE-GAN).The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions,and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution,initial reservoir performance and time-varying well control,thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions.The experimental results in basic(egg model)and actual water-flooding reservoirs show that the model predictions align well with numerical simulations.The median of relative residuals of pressure and oil saturation for the basic reservoir model are 0.5％and 9.0％,respectively,while those for the actual reservoir model are both 4.0％.Regarding time efficiency,the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models,respectively,compared with traditional numerical simulations.The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization.
deep generative networksurrogate modeltime-varying well controlwater-floodingreservoir performance
万方数据
维普
