@conference {herrmann2026SIAMGScud,
	title = {Context- and uncertainty-aware Digital Twin for the Optimization of Underground Storage Operations},
	booktitle = {SIAM Geosciences Webinar Series},
	year = {2026},
	month = {5},
	abstract = {Geological Carbon Storage (GCS) is the only truly scalable pathway to net-negative emissions on the path to net-zero, yet ensuring safe, cost-efficient, and verifiable GCS demands far more than static reservoir models. We introduce an uncertainty-aware Digital Twin framework that delivers continuous, probabilistic monitoring of subsurface CO$_{2}$ plume dynamics and enables risk-informed operational decisions in real time. At the core of our approach is a machine-learning-enabled data assimilation framework where Simulation-Based Inference (SBI) replaces linear ensemble Kalman filtering, enabling the framework to handle highly nonlinear CO$_{2}$ dynamics and time-lapse seismic and well-bore observations. This allows the Digital Twin to characterize the full posterior distribution of reservoir states{\textemdash}fluid saturations and pressures{\textemdash}conditioned on multimodal time-lapse observations from imaged surface seismic data and well measurements. The framework operates recursively: neural networks are trained offline on ensembles of simulated reservoir states and simulated observations, capturing complex nonlinear subsurface dynamics and observations. Once trained, the Digital Twin assimilates incoming field data in near real-time, continuously updating the probabilistic reservoir model as new measurements arrive. By maintaining a probabilistic rather than deterministic view of reservoir state, our Digital Twin enables decision-making under uncertainty{\textemdash}optimizing CO$_{2}$ injection schedules while managing the risk of exceeding critical fracture pressures, and identifying monitoring strategies that maximally reduce uncertainty. Validated on realistic numerical simulations of GCS scenarios, this framework offers a concrete path toward safer, more transparent, and verifiable Geological Carbon Storage operations.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, SIAMGS, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMGS/2026/herrmann2026SIAMGScud},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {herrmann2025AIDT4EScad,
	title = {Context-aware Digital Twin for Underground Storage},
	booktitle = {Artificial Intelligence and Digital Twins for Earth Systems 2025, Austin, TX},
	year = {2025},
	month = {9},
	keywords = {AIDT4ES, Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AIDT4ES/2025/herrmann2025AIDT4EScad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ipsita Bhar and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {herrmann2025IMSIcad,
	title = {Context-Aware Digital Twin for Underground Storage Operations and Decision Making},
	booktitle = {Data Assimilation and Inverse Problems for Digital Twins Workshop 205, IMSI, Chicago},
	year = {2025},
	month = {10},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, decision making, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IMSI/2025/herrmann2025IMSIcad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ipsita Bhar and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@presentation {li2023ML4SEISMICmci,
	title = {Maximizing CO2 injectivity within fracture pressure},
	year = {2023},
	month = {11},
	abstract = {In geological carbon storage projects, optimizing CO2 injection strategies is paramount to enhance storage efficiency and prevent leakage. The objective is to maximize the CO2 injection volume without surpassing the fracture pressure. Traditional adjoint-based approaches necessitate extensive numerical simulations, leading to significant computational overhead. To circumvent this challenge, we introduce an optimization framework based on physics-informed deep convolutional neural networks. Trained on different permeability slices, our model can rapidly predict the maximal CO2 injection volume for new permeability fields in real-time.},
	keywords = {CCS, ML4SEISMIC, Optimization, reservoir simulation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/li2023ML4SEISMICmci},
	author = {Haoyun Li and Ziyi Yin and Olav M{\o}yner and Felix J. Herrmann}
}
