Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models

TitleReconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models
Publication TypePresentation
Year of Publication2024
AuthorsHaoyun Li, Zeng, S, Abhinav Prakash Gahlot, Felix J. Herrmann
Keywordsaugmentation, 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.

URLhttps://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/li2024ML4SEISMICrps
Citation Keyli2024ML4SEISMICrps