Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models
Title | Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models |
Publication Type | Presentation |
Year of Publication | 2024 |
Authors | Haoyun Li, Zeng, S, Abhinav Prakash Gahlot, Felix J. Herrmann |
Keywords | augmentation, Bayesian inference, deep learning, diffusion models, Inverse problems, ML4SEISMIC, SLIM, Summary Statistics, two-phase flow, Uncertainty quantification |
Abstract | This study explores the application of a diffusion partial differential equation (PDE) model for reservoir simulation, particularly aimed at reconstructing permeability and saturation fields within a saline aquifer. Focusing on pairs of input permeability and output saturation, the model is trained to capture the underlying dynamics governing fluid flow in porous media. Post-training, the model is capable of inferring or recovering the complete permeability and saturation distributions when provided with limited vertical pixel data of permeability and saturation. This approach offers a novel pathway for enhancing the resolution of subsurface characteristics, contributing to more accurate predictions in reservoir engineering and carbon storage simulations. |
URL | https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/li2024ML4SEISMICrps |
Citation Key | li2024ML4SEISMICrps |