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.

翻译：基于有限测量数据生成准确且地质上合理的储层相，对于油田开发与储层管理至关重要，尤其是在油气领域。传统两点地质统计学虽为基础方法，但往往难以捕捉复杂地质模式。多点地质统计学提供了更多灵活性，却面临自身挑战。随着生成对抗网络（GANs）的兴起及其在各领域的成功应用，学界开始转向利用GAN进行相生成。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GAN。受此启发，本文提出一种专为条件性储层相生成设计的新型潜在扩散模型。该模型可生成高保真度的相实现，并严格保留条件化数据，其性能显著优于基于GAN的替代方法。