Facies are rock bodies that reflect specific depositional environments and play a central role in reservoir characterization. Accurate facies modeling is a key challenge in generating realistic geological scenarios that honor sparse well data while capturing geological uncertainty. This study introduces FaciesGAN, a novel deep learning framework based on conditional generative adversarial networks (cGANs). The method employs a hierarchical structure of generators and discriminators that progressively refine coarse estimates into high-resolution facies models, ensuring consistency with well data and depositional patterns at each stage. FaciesGAN was validated using the limited Stanford Earth Science Data dataset, demonstrating strong performance even under data scarcity. The quantitative evaluation employed multidimensional scaling and yielded an intersection over union index of 99.96% relative to the conditioning well data. These results confirmed the model’s ability to generate diverse scenarios with high fidelity while preserving statistical distributions. Compared with a traditional multiple-point statistics implementation, FaciesGAN produced more realistic and varied geological realizations with significantly greater computational efficiency. These results indicate that cGAN-based approaches, such as FaciesGAN, represent a promising direction for subsurface modeling, offering robust tools for data augmentation, improved uncertainty assessment, and enhanced reservoir characterization.