Latent diffusion model for conditional reservoir facies generation
Dae-Soo Lee, Oscar Ovanger, Jo Eidsvik, Erlend Aune, Jacob Skauvold, Ragnar Hauge
Abstract
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: https://released-on-accept . • The first paper to adopt a diffusion model for conditional facies generation, • A novel latent diffusion model, designed for maximal preservation of conditional facies data in generated samples. • Conditional facies generation with high fidelity, sample diversity, and the robust preservation of conditional data.