@conference {zeng2025ML4SEISMICrrs,
	title = {Reconstructing reservoir states from multimodal data via score-based generative models},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {8},
	abstract = {This study develops a score-based generative framework for reservoir simulation to reconstruct spatially varying permeability and saturation distributions in saline aquifers from sparse observations at two wells combined with time-lapse seismic data. We learn joint distributions of permeability, saturation, and pressure from high-fidelity multiphase-flow simulations, conditioned on observed imaged time-lapse seismic data. The framework provides a unified, any-subset inference interface that flexibly incorporates well logs and physics-based constraints to enforce mass balance and physically plausible behavior. An ablation without pressure on training yields markedly lower structural similarity within physically meaningful measurement ranges, underscoring the pressure{\textquoteright}s importance for accurate reconstruction of the saturation. Our approach generalizes across varying geological settings and highlights the value of multimodal data fusion for practical reservoir management.},
	keywords = {deep learning, diffusion-pde models, Generative models, history matching, ML4SEISMIC, reservoir simulation, score, SLIM, sparse observations, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/zeng2025ML4SEISMICrrs},
	author = {Shiqin Zeng and Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@presentation {li2024ML4SEISMICrps,
	title = {Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models},
	year = {2024},
	month = {11},
	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.},
	keywords = {augmentation, Bayesian inference, deep learning, diffusion models, Inverse problems, ML4SEISMIC, SLIM, Summary Statistics, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/li2024ML4SEISMICrps},
	author = {Haoyun Li and Shiqin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
