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Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous media

Shams, R ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.petrol.2019.106794
  3. Publisher: Elsevier B.V , 2020
  4. Abstract:
  5. In this study, coupled Generative Adversarial and Auto-Encoder neural networks have been used to reconstruct realizations of three-dimensional porous media. The gradient-descent-based optimization method is used for training and stabilizing the neural networks. The multi-scale reconstruction has been conducted for both sandstone and carbonate samples from an Iranian oilfield. The sandstone contains inter and intra-grain porosity. The generative adversarial network predicts the inter-grain pores and the auto-encoder provides the generative adversarial network result with intra-grain pores (micro-porosity). Different matching criteria, including porosity, permeability, auto-correlation function, and visual interpretation have been used to investigate the performance of the models. This methodology provides researchers with a reliable method to reconstruct multi-scale realizations of porous media. © 2019 Elsevier B.V
  6. Keywords:
  7. Auto-encoder neural network ; Generative adversarial network ; Machine learning ; Multi-scale reconstruction ; Porous media reconstruction ; Gradient methods ; Learning systems ; Porosity ; Porous materials ; Sandstone ; Adversarial networks ; Auto encoders ; Autocorrelation functions ; Matching criterion ; Reliable methods ; Visual interpretation ; Network coding ; Artificial neural network ; Carbonate ; Oil field ; Porous medium ; Reconstruction ; Sandstone ; Three-dimensional modeling
  8. Source: Journal of Petroleum Science and Engineering ; Volume 186 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0920410519312124