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.