Latent Diffusion Model for Conditional Reservoir Facies Generation
Daesoo Lee Oscar Ovanger Jo Eidsvik Erlend Aune Jacob Skauvold Ragnar Hauge
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.