@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}
}
@conference {li2025ML4SEISMICric,
	title = {Risk-Aware Injectivity Control for Energy Storage},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {We introduce a digital-twin{\textendash}driven framework for real-time injectivity control that maximizes injected CO2 while enforcing reservoir safety. The forward model is a multiphase flow simulator evolving reservoir states under injectivity controls q; time-lapse seismic acts as the observation operator to reduce uncertainty of the the state (reservoir{\textquoteright}s saturation/pressure). Risk is handled via two complementary metrics: the Probability of Failure (POF) for exceedance likelihood and the less conservative CVaR (superquantile) for tail severity. For the optimization, we employ smooth surrogates{\textemdash}a logistic exceedance rate for POF and the Rockafellar{\textendash}Uryasev formulation for CVaR{\textemdash}enabling gradient-based control with constraints. Within a finite-horizon Model Predictive Control (MPC) loop, we optimize a trajectory per posterior sample and aggregate the ensemble of optimal rates into an empirical (KDE-kernel) Cumulative Density Function (CDF) with binomial confidence bands; the final controlled injectivity q is chosen as the largest rate whose upper confidence bound satisfies the target risk level. Case studies demonstrate that POF-constrained optimization is more conservative than CVaR-based optimization, In addition, sensitivity of the policy to the CVaR tail level, α, is reduced compared to POF{\textquoteright}s parameter sensitivity, allowing for adaptive, uncertainty-aware control that balances injectivity and fracture risk. The proposed framework also unifies time-lapse seismic-informed inference, risk metrics, and MPC into a practical approach for safe high-throughput underground energy storage.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, EOR, experimental design, FWI, GCS, Imaging, Inverse problems, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/li2025ML4SEISMICric},
	author = {Haoyun Li and Abhinav Prakash Gahlot 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}
}
@presentation {louboutin2021ML4SEISMICrla,
	title = {Randomized linear algebra for inversion},
	year = {2021},
	month = {11},
	abstract = {Inverse problems in exploration geophysics or machine learning heavily relies on linear algebra and large matrices manipulations. To tackle the growing cost of storing these matrices, randomized algorithms have been developed to obtain information from these matrices via randomized sketching. Inspired by previous work on extended image volumes, we will first show in this talk how the seismic imaging condition can be expressed in a randomized linear algebra framework leading to drastic memory savings. In a second part, we will extend this idea to convolutional neural networks to reduce the memory cost of training by orders of magnitude. We will demonstrate the practicality of these methods on representative examples.},
	keywords = {FWI, HPC, inversion, ML4SEISMIC, randomized linear algebra, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICrla/Tue-10-20-Louboutin.pdf},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {witte2021ML4SEISMICrtc,
	title = {Redwood {\textendash} towards clusterless supercomputing in the cloud},
	year = {2021},
	month = {11},
	abstract = {We present Redwood, a Julia framework for clusterless supercomputing in the cloud. Redwood provides a set of distributed programming macros that enable users to remotely execute Julia functions in parallel through cloud services for batch and serverless computing. We present the architecture and design of Redwood, as well as its application to existing Julia packages for machine learning and inverse problems.},
	keywords = {cloud computing, clusterless, HPC, Julia, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/witte2021ML4SEISMICrtc/Mon-10-30-Witte.pdf},
	author = {Philipp A. Witte}
}
