@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 {gahlot2026SIAMUQdmc,
	title = {Decision Making and Control with Digital Twins for Underground Energy Storage},
	booktitle = {SIAM Conference on Uncertainty Quantification (UQ26), Minneapolis, MN},
	year = {2026},
	month = {3},
	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, SIAMUQ, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2026/gahlot2026SIAMUQdmc},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@conference {herrmann2026HALuad,
	title = {Uncertainty-aware Digital Twins for Monitoring and Optimizing of Underground Energy Operations},
	booktitle = {Landmark Innovation Forum \& Expo 2026, LIFE2026, Orlando, FL},
	year = {2026},
	month = {4},
	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, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2026/herrmann2026HALuad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@conference {herrmann2026SEGWSudt,
	title = {Uncertainty-aware Digital Twins for Monitoring and Optimizing of Geological Carbon Storage},
	booktitle = {Net-Zero Emissions: Critical Minerals, Hydrogen, Nuclear, Geothermal, Wind, CCUS, and AI, SEG Workshop},
	year = {2026},
	month = {3},
	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 seismic 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, SEG, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2026/herrmann2026SEGWSudt},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@conference {herrmann2025SEGSPEcua,
	title = {Context- and uncertainty-aware Digital Twins for Integrated Reservoir Management},
	booktitle = {SEG/SPE 2nd Integrated Reservoir Characterization, Surveillance, and Management Workshop: Advances and Challenges},
	year = {2025},
	month = {7},
	abstract = {We present a new framework for Digital Twins (DTs) that supports uncertainty-aware monitoring, optimization, and risk management of underground storage operations, with a particular focus on Geological Carbon Storage (GCS) and Enhanced Oil Recovery (EOR). The proposed DT addresses key challenges in reservoir characterization, surveillance, and management by integrating generative AI methods with simulation-based inference, enabling scalable, probabilistic decision support under uncertainty. Our approach combines sensitivity-aware amortized Bayesian inference (SA-ABI) with ensemble-based data assimilation to efficiently incorporate multimodal time-lapse observations, such as seismic and well data, into dynamic reservoir models. By embedding context-awareness into neural networks, we account for variability in rock-physics relationships linking pressure, saturation, and seismic responses, and quantify uncertainties arising from model misspecifications{\textendash}-e.g. usage of patchy versus uniform saturation models. Computational efficiency is maintained through weight sharing across the network trained on different rock-physics scenarios, enabling rapid evaluation of the impact changing the underlying rock-physics model. The DT is built on industry-strength open-source software: {\textquoteleft}JutulDarcy.jl{\textquoteleft} for fully coupled multiphase flow simulations and {\textquoteleft}JUDI.jl{\textquoteleft}, a seismic inversion package leveraging the {\textquoteleft}Devito{\textquoteleft} finite-difference framework. This computational foundation allows scalable integration of physics-based simulations and data assimilation across large reservoir models, while retaining flexibility to adapt to varying physical contexts such as the inclusion of pressure-dependence of the compressional wavespeed. The resulting DT not only delivers posterior distributions for the reservoir{\textquoteright}s state (pressure/saturation) conditioned on available observations but also enables rapid {\textquotedblleft}what-if{\textquotedblright} scenario analysis by drawing posterior samples across a range of physical contexts without retraining. This capability supports proactive storage management, optimization of injection operations, and improved assurance of storage conformance.},
	keywords = {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, SEG, SPE, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEGSPE/2025/herrmann2025SEGSPEcua},
	author = {Abhinav Prakash Gahlot and Ipsita Bhar and Haoyun Li 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}
}
@conference {gahlot2025CCUSocs,
	title = {Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling and Optimal Pressure Control},
	booktitle = {CCUS 2025 - Carbon Capture, Utilization, and Storage Conference},
	year = {2025},
	month = {3},
	abstract = {Based on the latest data-assimilation and machine-learning techniques, Digital Twins (DTs) have shown promise for high-fidelity monitoring and control of underground CO2​ storage. While the use of these techniques have important advantages, they do rely on certain assumptions. If these assumptions are not met, the DT{\textquoteright}s neural networks may no longer infer the state of the CO2 plume (pressure/saturation) accurately. By augmenting the forecast ensemble, we address this issue.},
	keywords = {Bayesian inference, CCUS, conditional normalizing flows, control, deep learning, digital twin, GCS, rock physics, sequential Bayes, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2025/gahlot2025CCUSocs},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@presentation {herrmann2024ML4SEISMICdtg,
	title = {A Digital Twin for Geological Carbon Storage with Controlled Injectivity},
	year = {2024},
	month = {11},
	abstract = {We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO 2  injectivity to mitigate reservoir fracturing risks. In GCS, DT represents virtual replicas of subsurface systems that incorporate real-time data and advanced generative Artificial Intelligence (genAI) techniques, including neural posterior density estimation via simulation-based inference and sequential Bayesian inference. These methods enable the effective monitoring and control of CO 2  storage projects, addressing challenges such as subsurface complexity, operational optimization, and risk mitigation. By integrating diverse monitoring data, e.g., geophysical well observations and imaged seismic, DT can bridge the gaps between seemingly distinct fields like geophysics and reservoir engineering. In addition, the recent advancements in genAI also facilitate DT with principled uncertainty quantification. Through recursive training and inference, DT utilizes simulated current state samples, e.g., CO 2  saturation, paired with corresponding geophysical field observations to train its neural networks and enable posterior sampling upon receiving new field data. However, it lacks decision-making and control capabilities, which is necessary for full DT functionality. This study aims to demonstrate how DT can inform decision-making processes to prevent risks such as cap rock fracturing during CO 2  storage operations.},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, Imaging, Inverse problems, ML4SEISMIC, rock physics, RTM, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/herrmann2024ML4SEISMICdtg},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
