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    GANs for 3D Porous media generation

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    Miguel Corrales_GANs for 3D Porous media generation.pdf
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    Type
    Poster
    Authors
    Corrales, Miguel
    Date
    2022-11-15
    Permanent link to this record
    http://hdl.handle.net/10754/685699
    
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    Abstract
    Linking the fluid flow at the pore scale and reservoir scale is an active area of research in projects related to CO2 storage and oil and gas recovery. A key obstacle to understanding such a process is the lack of physical samples from relevant geological areas. This issue can be addressed by generating accurate, digital representations of the rock samples available for numerical fluid flow simulations. A new promising avenue for generating realistic digital rock samples is opening up because of recent advancements in Machine Learning and Deep Generative Modeling. In particular, Generative Adversarial Networks (GANs) can learn complex distributions with high dimensions and produce high-quality samples. This study presents a Wasserstein GAN with gradient penalty (WGAN-GP) to generate high-quality porous media samples in 3D. Additionally, an evaluation metric set inspired by geometry, topology, and fluid flow properties is established to assess the generative quality.
    Conference/Event name
    KAUST Research Conference SCML2033
    Collections
    KAUST Research Conference SCML2022; Posters

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