1887

Abstract

Summary

Current micro-CT image resolution is limited to ∼1-2 microns. A recent study has identified that at least 10 image voxels are needed to resolve pore throats, which limits the applicability of direct simulations using the Digital Rock (DR) technology to medium-to-coarse grained rocks (i.e., rocks with permeability > 100 mD). On the other hand, 2D high-resolution colored images such as the ones obtained from Scanning Electron Microscopy (SEM) deliver a much higher resolution (∼0.5 microns). However, reliable and efficient workflows to jointly utilize full-size SEM images, measured 3D core-plug permeabilities, and 2D direct pore-scale flow simulations on SEM images within a predictive framework for permeability estimation are lacking. In order to close this gap, we introduce a Deep Learning (DL) algorithm for the direct prediction of permeability from SEM images. The trained DL model predicts properties accurately within seconds, and therefore, provide a significant speeding up simulation workflows. Preliminary results will be presented and discussed.

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/content/papers/10.3997/2214-4609.201802173
2018-09-03
2019-12-15
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References

  1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y. and Zheng, X.
    [2016] TensorFlow: A System for Large-scale Machine Learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI’16. USENIX Association, Berkeley, CA, USA, 265–283.
    [Google Scholar]
  2. Adler, A., Boublil, D. and Zibulevsky, M.
    [2017] Block-based compressed sensing of images via deep learning. In: 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). 1–6.
    [Google Scholar]
  3. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T.
    [2018] Deep-learning tomography. The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  4. Bergstra, J., Bardenet, R., Bengio, Y. and Kégl, B.
    [2011] Algorithms for Hyper-parameter Optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS’11. Curran Associates Inc., USA, 2546–2554.
    [Google Scholar]
  5. Bhatnagar, P., Gross, E. and Krook, M.
    [1954] A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems. Physical Review, (94), 511–525.
    [Google Scholar]
  6. Cancelliere, A., Chang, C., Foti, E., Rothman, D. and Succi, S.
    [1990] The permeability of a random medium: comparison of simulation with theory. Physics of Fluids, (2), 2085–2088.
    [Google Scholar]
  7. Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B. and Shelhamer, E.
    [2014] cuDNN: Efficient Primitives for Deep Learning. ArXiv e-prints.
    [Google Scholar]
  8. Chollet, F. et al.
    [2015] Keras. https://keras.io.
  9. d’Humières, D., Ginzburg, I., Krafczyk, M., Lallemand, P. and Luo, L.
    [2002] Multiple-Relaxation-Time lattice Boltzmann models in three dimensions. Philosophical Transactions of the Royal Society, (360), 437–451.
    [Google Scholar]
  10. Ferrèol, B. and Rothman, D.
    [1995] Lattice-Boltzmann simulations of flow through Fontainebleau sandstone. Transport in Porous Media, (20), 3–20.
    [Google Scholar]
  11. Goodfellow, I., Bengio, Y. and Courville, A.
    [2016] Deep Learning. MIT Press.
    [Google Scholar]
  12. Gray, F. and Boek, E.
    [2016] Enhancing Computational Precision for Lattice Boltzmann Schemes in Porous Media Flows. Computation, (4), 11.
    [Google Scholar]
  13. He, X. and Luo, L.
    [1997] Theory of the lattice Boltzmann method: From the Boltzmann equation to the lattice Boltzmann equation. Physical Review, (56), 6811–6817.
    [Google Scholar]
  14. Hornik, K., Stinchcombe, M. and White, H.
    [1989] Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366.
    [Google Scholar]
  15. Keehm, Y.
    [2003] Computational rock physics: Transport properties in porous media and applications. Ph.D. Dissertation, Stanford University, Stanford, California, U.S.A.
    [Google Scholar]
  16. Lallemand, P. and Luo, L.
    [2000] Theory of the lattice Boltzmann method: dispersion, dissipation, isotropy, Galilean invariance, and stability. Physical Review, (61), 6546–6562.
    [Google Scholar]
  17. LeCun, Y., Bengio, Y. and Hinton, G.
    [2015] Deep learning. Nature, 521, 436.
    [Google Scholar]
  18. Martys, N. and Chen, H.
    [1996] Simulation of multicomponent fluids in complex three dimensional geometries by the lattice Boltzmann method. Physical Review, (53), 743–750.
    [Google Scholar]
  19. Premnath, K. and Abraham, J.
    [2007] Three-dimensional multi-relaxation-time (MRT) lattice-Boltzmann models for multiphase flow. Journal of Computational Physics, (224), 539–559.
    [Google Scholar]
  20. Ronneberger, O., Fischer, P. and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M. and Frangi, A.F. (Eds.) Medical Image Computing and Computer-Assisted Intervention –MICCAI 2015. Springer International Publishing, 234–241.
    [Google Scholar]
  21. Ruder, S.
    [2016] An overview of gradient descent optimization algorithms. ArXiv e-prints.
    [Google Scholar]
  22. Saxena, N., Mavko, G., Hofmann, R. and Srisutthiyakorn, N.
    [2017] Estimating permeability from thin sections without reconstruction: Digital rock study of 3D properties from 2D images. Computers and Geosciences, (102), 79–99.
    [Google Scholar]
  23. Sochi, T.
    [2010] Pore-Scale Modeling of Non-Newtonian Flow in Porous Media. Ph.D. thesis, PhD Thesis, 2010.
    [Google Scholar]
  24. Srisutthiyakorn, N.
    [2016] Deep-learning methods for predicting permeability from 2D/3D binary-segmented images. 3042–3046.
    [Google Scholar]
  25. Succi, S.
    [2003] The Lattice Boltzmann Equation - For Fluid Dynamics and Beyond. 22, 368.
    [Google Scholar]
  26. Sudakov, O., Burnaev, E. and Koroteev, D.
    [2018] Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks. ArXiv e-prints.
    [Google Scholar]
  27. Sukop, M.C. and Thorne, D.T.
    [2010] Lattice Boltzmann Modeling: An Introduction for Geoscientists and Engineers. Springer Publishing Company, Incorporated, 1st edn.
    [Google Scholar]
  28. Sutskever, I., Martens, J., Dahl, G. and Hinton, G.
    [2013] On the Importance of Initialization and Momentum in Deep Learning. In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, ICML’13. JMLR.org, III–1139–III–1147.
    [Google Scholar]
  29. Wang, G.
    [2016] A Perspective on Deep Imaging. IEEE Access, 4, 8914–8924.
    [Google Scholar]
  30. Würfl, T., Hoffmann, M., Christlein, V., Breininger, K., Huang, Y., Unberath, M. and Maier, A.K.
    [2018] Deep Learning Computed Tomography: Learning Projection-Domain Weights from Image Domain in Limited Angle Problems. IEEE Transactions on Medical Imaging, 1–1.
    [Google Scholar]
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