Abstract
In digital rock physics,the study of physical parameters and flow characteristics of reservoirs requires a wealth of three-dimension digital rock samples.However,traditional physical methods of obtaining digital rock are expensive,and numerical reconstruction method cannot obtain a reasonable pore structure for complex rock.Recently,generative adversarial networks(GANs)have proven to be a successful method for reconstructing pore-scale models,but the reconstructed large-size digital rocks look unreasonable due to the lack of sufficient consideration of multi-scale information fusion and it takes a lot of computational resources and time to build the model.Hence,we proposed a combination of InfoGAN and style-based GAN guided by prior information(CIS-GAN)to reconstruct more controllable and reasonable digital rock models using small set of samples.Porosity distribution as prior information is added into latent space to control pore distribution,and a classifier Q is set in discriminator to ensure the porosity is limited to reasonable range.Multi-scale information is applied each layer by style transfer and used to optimize the background information,pore structure,and micro information of model for multi-scale fusion to produce more reasonable and natural digital rock.Simple-structure sandstone and complex-structure carbonate are implemented to test the reconstruction ability of network.The result shows that synthetic sample has high consistency on pore-throat geometry and connectivity,and the CISGAN can produce natural and user-specified digital rock samples.And the CISGAN will provide more reasonable and various type samples for intelligent parameter prediction of digital rock.
NSTL