Skip to main content

Advertisement

SpringerLink
Log in

A pore space reconstruction method of shale based on autoencoders and generative adversarial networks

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

Shale oil and shale gas, as widely distributed unconventional resources, have attracted much attention at present due to the fast-increasing demands for energy. Since shale is the storage medium for shale oil and gas, its microscopic characteristics surely will influence its own storage ability and the difficulty of exploitation. The internal structures of shale can be studied based on establishing a digital core that can describe the characteristics of shale. There are two mainstream methods for the reconstruction of digital cores: the physical experimental methods and numerical reconstruction methods. The physical experimental methods usually need real rock samples as experimental materials, causing the high cost of experimental equipment and great difficulty of preparing the samples, especially the fragile samples such as shale. Numerical reconstruction methods are therefore often combined with the physical experimental methods to reconstruct digital cores, while the traditional numerical methods suffer from numerous reconstruction time. Recently, with the rapid development of deep learning and its branches, e.g., generative adversarial networks (GANs), the reconstruction method of digital cores possibly can benefit from the inherent ability of extracting characteristics by GAN. However, GAN may have slow training speed and unstable training process, so autoencoders (AEs) are introduced to address the issue of GAN. In this paper, combining the advantages of the two models (i.e., AE and GAN), a method AE-GAN is proposed to implement the 3D reconstruction of shale, and the effectiveness of this method is proven by comparing to some typical numerical methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Boersma, T., Johnson, C.: The shale gas revolution: U.S. and EU policy and research agendas. Rev. Policy Res.. 29(4), 570–576 (2016)

  2. Chen, S.B., Zhu, Y.M., Wang, G.Y., Liu, H.L., Fang, J.H.: Structure characteristics and accumulation significance of nanopores in Longmaxi shale gas reservoir in the southern Sichuan basin. J. China Coal Soc. 37(3), 438–444 (2012)

    Google Scholar 

  3. Torquato, S., Haslach, H.: Random heterogeneous materials: microstructure and macroscopic properties. Appl. Mech. Rev. 55(4), 1–41 (2002). https://doi.org/10.1115/1.1483342

    Article  Google Scholar 

  4. Liang, Z.R., Fernandes, C.P., Magnani, F.S., Philippi, P.C.: A reconstruction technique for three-dimensional porous media using image analysis and fourier transforms. J. Pet. Sci. Eng. 21(3), 273–283 (1998)

    Article  Google Scholar 

  5. Bryant, S., Blunt, M.: Prediction of relative permeability in simple porous media. Phys. Rev. A. 46, 2004–2011 (1992)

    Article  Google Scholar 

  6. Hazlett, R.D.: Statistical characterization and stochastic modeling of pore networks in relation to fluid flow. Math. Geol. 29(6), 801–822 (1997)

    Article  Google Scholar 

  7. Quiblier, J.A.: A new three-dimensional modeling technique for studying porous media. J. Colloid Interface Sci. 98(1), 84–102 (1984)

    Article  Google Scholar 

  8. Strebelle, S.: Conditional simulation of complex geological structures using multiple-point statistics. Math. Geol. 34(1), 1–21 (2002)

    Article  Google Scholar 

  9. Zhang, T., Switzer, P., Journel, A.: Filter-based classification of training image patterns for spatial simulation. Math. Geol. 38(1), 63–80 (2006)

    Article  Google Scholar 

  10. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial nets. In Advances in neural information processing systems 27 (NIPS 2014). 2672–2680 (2014)

  11. Wang, Y.D., Blunt, M.J., Armstrong, R.T., Mostaghimi, P.: Deep learning in pore scale imaging and modeling. Earth Sci. Rev. 215, 103555 (2021)

    Article  Google Scholar 

  12. Mosser, L., Dubrule, O., Blunt, M.J.: Reconstruction of three-dimensional porous media using generative adversarial neural networks. Phys. Rev. E. 96(4), 043309 (2017)

    Article  Google Scholar 

  13. Mosser, L., Dubrule, O., Blunt, M.J.: Stochastic reconstruction of an Oolitic limestone by generative adversarial networks. Transp. Porous Media. 125(1), 81–103 (2018)

    Article  Google Scholar 

  14. Feng, J., He, X., Teng, Q., Ren, C., Chen, H., Li, Y.: Accurate and fast reconstruction of porous media from extremely limited information using conditional generative adversarial network. Phys. Rev. E. 100(3), 33308 (2019). https://doi.org/10.1103/PhysRevE.100.033308

    Article  Google Scholar 

  15. Feng, J., Teng, Q., Li, B., He, X., Chen, H., Li, Y.: An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning. Comput. Methods Appl. Mech. Eng. 368(113043), 113043 (2020)

    Article  Google Scholar 

  16. Zhu, J., Zhang, R., Pathak, D., Darrell, T., Efros, A.A., Wang, O., Shechtman, E.: Toward multimodal image-to-image translation. Advances in Neural Information Processing Systems. 2017-December(1 of 1), 466–477 (2017)

  17. Valsecchi, A., Damas, S., Tubilleja, C., Arechalde, J.: Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks. Neurocomputing. 399, 227–236 (2020)

    Article  Google Scholar 

  18. Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. Comput. Sci. 2672–2680, 2014

  19. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

  20. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. ArXiv, abs/1701.07875 (2017)

  21. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-January(1 of 1), 105–114 (2017)

  22. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science. 313(5786), 504–507 (2006). https://doi.org/10.1126/science.1127647

    Article  Google Scholar 

  23. He, K.M., Zhang, X.Y., Ren, S.Q., Sun, J.: Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 770–778 (2016)

  24. Mariethoz, G., Renard, P., Straubhaar, J.: The direct sampling method to perform multiple-point geostatistical simulations. Water Resour. Res. 46(11), 1–14 (2010)

    Google Scholar 

  25. Li, Z., Zhang, X., Clarke, K.C., Liu, G., Zhu, R.: An automatic variogram modeling method with high reliability fitness and estimates. Comput. Geosci. 120, 48–59 (2018)

    Article  Google Scholar 

  26. Mariethoz, G., Caers, J.: Multiple-point Geostatistics: stochastic modeling with training images. John Wiley & Sons (2014)

  27. Dong, H., Blunt, M.J.: Pore-network extraction from micro-computerized-tomography images. Phys. Rev. E - Stat. Nonlinear Soft Matter Phys. 80(3), 36307 (2009)

    Article  Google Scholar 

  28. Singh, M., Mohanty, K.K.: Permeability of spatially correlated porous media. Chem. Eng. Sci. 55(22), 5393–5403 (2000). https://doi.org/10.1016/S0009-2509(00)00157-3

    Article  Google Scholar 

  29. Zhang, T., Du, Y., Huang, T., Yang, J., Lu, F., Li, X.: Reconstruction of porous media using ISOMAP-based MPS. Stoch. Env. Res. Risk A. 30(1), 395–412 (2016). https://doi.org/10.1007/s00477-015-1142-1

    Article  Google Scholar 

  30. FEI, C.: Avizo 9 User’s Guide, FEI Corporation (2009)

  31. Wang, Y.D., Shabaninejad, M., Armstrong, R.T., Mostaghimi, P.: Physical Accuracy of Deep Neural Networks for 2d and 3d Multi-Mineral Segmentation of Rock Micro-Ct Images. arXiv:2002.05322 (2021)

  32. Santos, J.E., Yin, Y., Jo, H., Pan, W., Kang, Q., Viswanathan, H.S., Prodanović, M., Pyrcz, M.J., Lubbers, N.: Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media. Transp. Porous Media. (2021). https://doi.org/10.1007/s11242-021-01617-y

Download references

Code availability

Not applicable.

Funding

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

Author information

Authors and Affiliations

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

    Ting Zhang, Deya Li & Fangfang Lu

Authors
  1. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deya Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangfang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fangfang Lu.

Ethics declarations

On behalf of, and having obtained permission from all the authors, I declare that:

• The manuscript has not been submitted to more than one journal for simultaneous consideration.

• The manuscript has not been published previously (partly or in full).

• Our study is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time.

• No data have been fabricated or manipulated (including images) to support our conclusions.

• No data, text, or theories by others are presented as if they were the author’s own. Proper acknowledgements have been given.

• Consent to submit has been received explicitly from all co-authors, as well as from the responsible authorities, before the work is submitted.

• Authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results.

• We have ensured the correct author group, corresponding author, and order of authors before submission.

The sources of funding have been listed in funding information. Our research did not involve human participants or animals. I testify to the accuracy of the above on behalf of all the authors.

Fangfang Lu (corresponding author).

14-April-2021.

Conflicts of interest/competing interests

The authors declare that we have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Li, D. & Lu, F. A pore space reconstruction method of shale based on autoencoders and generative adversarial networks. Comput Geosci 25, 2149–2165 (2021). https://doi.org/10.1007/s10596-021-10083-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-021-10083-w

Keywords

Search

Navigation