  Creating accurate and geologically realistic reservoir facies based on
limited measurements is crucial for field development and reservoir management,
especially in the oil and gas sector. Traditional two-point geostatistics,
while foundational, often struggle to capture complex geological patterns.
Multi-point statistics offers more flexibility, but comes with its own
challenges. With the rise of Generative Adversarial Networks (GANs) and their
success in various fields, there has been a shift towards using them for facies
generation. However, recent advances in the computer vision domain have shown
the superiority of diffusion models over GANs. Motivated by this, a novel
Latent Diffusion Model is proposed, which is specifically designed for
conditional generation of reservoir facies. The proposed model produces
high-fidelity facies realizations that rigorously preserve conditioning data.
It significantly outperforms a GAN-based alternative.
