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 related to pattern configurations and storage limits. 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. Our implementation on GitHub: \url{https://github.com/ML4ITS/Latent-Diffusion-Model-for-Conditional-Reservoir-Facies-Generation}.

翻译：基于有限测量数据创建准确且地质真实的储层相，对于油田开发和储层管理至关重要，在油气领域尤其如此。传统的两点地质统计学虽具基础性，但往往难以捕捉复杂的地质模式。多点统计学提供了更大的灵活性，但其自身在模式构型与存储限制方面存在挑战。随着生成对抗网络（GANs）的兴起及其在多个领域的成功应用，学界已转向利用其进行储层相生成。然而，计算机视觉领域的最新进展表明，扩散模型在性能上优于GANs。受此启发，本文提出了一种新颖的潜在扩散模型，该模型专为储层相的条件生成而设计。所提模型能够生成高保真度的储层相实现，并严格保持条件数据的约束。其性能显著优于基于GAN的替代方案。我们的实现代码已发布于GitHub：\url{https://github.com/ML4ITS/Latent-Diffusion-Model-for-Conditional-Reservoir-Facies-Generation}。