Subsurface fracture networks skip fracture-by-fracture sampling: a latent diffusion model generates native 3D voxel fields and honors 84.2% of sparse well voxels
Subsurface fracture networks were previously sampled fracture by fracture from prescribed statistics, which made sparse well observations hard to enforce. Here 2D and 3D networks are represented as binary occupancy fields, compressed by a variational autoencoder into a compact latent space, and learned by a diffusion model, with well observations injected through a learned conditioning encoder, classifier-free guidance, and diffusion posterior sampling. The 2D conditional model satisfies 99.6% of prescribed well observations, while the native 3D volumetric field satisfies 84.2% of observed well voxels while keeping fracture volume fraction close to the reference ensemble.