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Reconstruction of Porous Media Using an Information Variational Auto-Encoder

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Abstract

The reconstruction of porous media is significant to some fields such as the study of seepage mechanics and reservoir engineering. Traditional methods have challenges in reconstruction quality due to their insufficient learning ability and suffer from lengthy computational time in large-quantity reconstruction tasks since they cannot reuse the parameters or models established previously. As a branch of deep learning, the variational auto-encoder (VAE) has shown excellent performance in extracting characteristics reflecting the underlying data manifold through a set of possible variables based on a latent model. Fisher information can help to balance the encoder and decoder in information control, used to estimate the variance of maximum likelihood estimate equation. Therefore, this paper proposes a method to reconstruct porous media based on VAE and Fisher information. Firstly, the structural characteristics of porous media are studied by the neural networks of the encoder to obtain the mean and variance of these characteristics. Then, the random sampling is carried out to reconstruct the intermediate results, and the optimization function of the encoder is combined with Fisher information to optimize the network. Finally, the intermediate results are input into the decoder to reconstruct porous media, and the optimization function of the decoder combined with Fisher information optimizes the reconstructed results. This method is evaluated by comparing the variogram, multiple-point connectivity, permeability and porosity of sandstone samples with some other typical reconstruction methods, showing its good reconstruction quality and efficiency.

Article Highlights

  • The reconstruction by our method is better than that of traditional methods.

  • The proposed method can reuse the parameters or models established previously.

  • The proposed method has a faster training speed.

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Funding

This work is supported by the National Natural Science Foundation of China (Nos. 41672114, 41702148).

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Ting Zhang, Hongyan Tu & Pengfei Xia

  2. College of Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

    Yi Du

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  1. Ting Zhang
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  2. Hongyan Tu
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Correspondence to Yi Du.

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Zhang, T., Tu, H., Xia, P. et al. Reconstruction of Porous Media Using an Information Variational Auto-Encoder. Transp Porous Med 143, 271–295 (2022). https://doi.org/10.1007/s11242-022-01769-5

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