@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}
}
@unpublished {gahlot2026IMAGEirs,
	title = {Improving reservoir state prediction in digital shadows using Radon-transformed subsurface common-offset gathers},
	year = {2026},
	month = {3},
	abstract = {Digital shadows for subsurface monitoring use simulation-based inference to map seismic and well-log observations to evolving reservoir states such as CO 2  saturation and pressure. When the simulation forward models rely on simplified or misspecified rock physics, predictions become biased and uncertainty quantification degrades. We address this by combining rock-physics-aware amortized Bayesian inference with inference in the Radon domain of common-image gathers (CIGs). Angle-dependent information from Radon-transformed subsurface offset gathers improves separation of fluid and stress effects: fluids exhibit distinct amplitude-variation-with-angle (AVA) signatures, so conditioning the digital shadow on angle gathers yields more accurate and physically consistent joint estimates of saturation and pressure than conditioning on migrated stacks or offset-domain gathers. We train a context-aware amortized posterior estimator that conditions on seismic data and rock-physics context, enabling robustness to model misspecification and structured what-if analysis at inference time without retraining. On a synthetic 2D geological carbon storage scenario, conditioning on Radon-transformed CIGs improves joint saturation and pressure prediction relative to reverse-time migrated images, while context conditioning preserves interpretability and calibration under rock physics uncertainty.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, IMAGE, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/gahlot2026IMAGEirs/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/gahlot2026IMAGEirs/IMAGE_Abhinav.pdf},
	author = {Abhinav Prakash Gahlot 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}
}
@unpublished {gahlot2025EAGEagc,
	title = {Advancing Geological Carbon Storage Monitoring with 3D Digital Shadow Technology},
	year = {2025},
	month = {6},
	keywords = {3D digital shadow, Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, FWI, GCS, Imaging, Inverse problems, RTM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2025/gahlot2025EAGEagc/abstract.html},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Felix J. Herrmann}
}
@conference {li2025IMAGEceo,
	title = {CO2 Enhanced Oil Recovery Through Digital Shadow Modeling},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, EOR, experimental design, FWI, GCS, IMAGE, Imaging, Inverse problems, permeability, reservoir simulation, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/li2025IMAGEceo},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/li2025IMAGEceo/IMAGE_Haoyun.pdf},
	author = {Haoyun Li and Abhinav Prakash Gahlot 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 {li2025ML4SEISMICdsc,
	title = {Digital Shadow for CO2-based Enhanced Oil Recovery (CO2-EOR)},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {We present a Digital Shadow for CO2-based Enhanced Oil Recovery (CO2-EOR), a data-driven surrogate model that predicts reservoir dynamics by conditioning on time-lapse seismic observations. Built upon conditional normalizing flows, the Digital Shadow learns the posterior distribution of subsurface states{\textemdash}CO2 and oil saturation{\textemdash}conditioned on seismic data generated through coupled multiphase flow and rock-physics modeling. The framework integrates high-fidelity simulations with JutulDarcy.jl, nonlinear seismic forward modeling and imaging with JUDI.jl, and nonlinear rock-physics mappings to relate saturation changes to changes in the acoustic properties. Trained on 128 stochastic realizations derived from a North Sea permeability distribution, the model achieves accurate posterior samples for the saturation fields with high structural similarity to the ground truth. This work demonstrates how integrating differentiable physics with generative modeling enables seismic-informed monitoring of CO2 plume evolution and oil recovery, laying the foundation for a Digital Twin of CO2-based EOR operations.},
	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/li2025ML4SEISMICdsc},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Olav Moyner and Felix J. Herrmann}
}
@unpublished {gahlot2025EAGEerd,
	title = {Enhancing robustness of Digital Shadow for CO 2  Storage Monitoring with Augmented Rock Physics Modeling},
	year = {2025},
	month = {6},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, GCS, Imaging, Inverse problems, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2025/gahlot2025EAGEerd/abstract.html},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {gahlot2025SIAMiac,
	title = {It{\textquoteright}s All in the Context: Sensitivity-Aware Digital Twins for Underground Storage},
	booktitle = {SIAM Conference on Mathematical \& Computational Issues in the Geosciences, Baton Rouge},
	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, SIAM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2025/gahlot2025SIAMiac},
	author = {Abhinav Prakash Gahlot and Ipsita Bhar and Felix J. Herrmann}
}
@conference {gahlot2025ML4SEISMICmrm,
	title = {Mitigating Rock-Physics Model Misspecification in Digital Shadows via Amortized Bayesian Inference},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Digital shadows for subsurface monitoring rely on simulation-based inference to map seismic observations to evolving reservoir states. However, when the simulations are generated using simplified or different rock physics models than those governing the observed data, model misspecification leads to biased posterior estimates and degraded uncertainty quantification. We address this challenge through Bayesian frameworks including rock-physics marginalization and a context-aware amortized inference using conditional normalizing flows. The framework learns a mapping from seismic responses to pressure and saturation while accounting for variability across different randomly drawn models for the rock-physics. To enhance sensitivity to fluid and stress effects, we also incorporate pressure-dependent rock physics into the seismic forward model and perform inference in the Radon domain of Common Image Gathers (CIGs) instead of conventional RTM images. Because fluids exhibit distinct angle-dependent signatures in AVA responses, Radon-transformed CIGs are expected to provide improved separation between pressure and saturation effects, leading to more accurate and physically consistent state estimates. Thus, it enables an uncertainty-aware, physics-informed digital shadows under rock-physics model mismatch.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, Imaging, Inverse problems, ML4SEISMIC, permeability, reservoir simulation, rock physics, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/gahlot2025ML4SEISMICmrm},
	author = {Abhinav Prakash Gahlot and Ipsita Bhar 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}
}
@conference {henriksson2025ML4SEISMICpss,
	title = {Predicting Subsurface CO2 Saturation using Late Fusion of Seismic and Streaming Well Data},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Determining subsurface flow of CO2 in porous rock formations is a challenging task especially when it involves intergration of multimodal data that are collected at disparate timescales. In this work, we investigate how data fushion can be used to integrate multimodal data consisting on infrequently collected active-source seismic surveys and continuous streaming data of saturation measurements collected at monitoring wells. For the purpose, we propose a data fusion framework based on late fusion, which combines seismic images, computed from seismic surveys, with time-series data collected at the wells. By integrating the spatial information from seismic data with high-resolution but sparce spatial-temporal patterns from wells, we aim to better approximate the complex CO2 flow patterns.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, GCS, Imaging, Inverse problems, late fusion, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/henriksson2025ML4SEISMICpss},
	author = {Victor Henriksson and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@unpublished {deng2025EAGEpjrm,
	title = {Probabilistic Joint Recovery Method for CO2 Plume Monitoring},
	year = {2025},
	month = {6},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, image generative model, Imaging, Inverse problems, JRM, Summary Statistics, Uncertainty quantification},
	doi = {10.48550/arXiv.2501.18761},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2025/deng2025EAGEpjrm/EAGE_Venice.pdf},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {park2025IMAGErdn,
	title = {A reduced-order derivative-informed neural operator for subsurface fluid-flow},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, IMAGE, Inverse problems, permeability, reduced-order modeling, reservoir simulation, SEG, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/park2025IMAGErdn/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot 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}
}
@article {bruer2024smpd,
	title = {Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering},
	journal = {Transactions on Geoscience and Remote Sensing},
	year = {2025},
	note = {(Accepted in Transactions on Geoscience and Remote Sensing)},
	month = {5},
	abstract = {Monitoring carbon dioxide (CO2) injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO2 injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO2 plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO2 monitoring domain by applying this scalable DA algorithm to a high-dimensional CO2 reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO2 saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO2 reservoir monitoring.},
	keywords = {Ensebmle Kalman Filter, GCS, Inverse problems, non-linear dynamical systems, seismic},
	doi = {10.48550/arXiv.2409.05193},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Edmond Chow and Felix J. Herrmann}
}
@presentation {bruer2025GSCSsmc,
	title = {Seismic monitoring of CO2 using ensemble Kalman filtering},
	year = {2025},
	note = {(GSCS, Gatech)},
	month = {02},
	keywords = {CCS, EnKF, fluid flow, GCS, GSCS, Imaging, monitoring, RTM, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Posters/GSCS/2025/bruer2025GSCSsmc/2025GSCS_poster.pdf},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Felix Herrmann and Edmond Chow}
}
@conference {gahlot2025IMAGEsrp,
	title = {Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, IMAGE, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	doi = {10.48550/arXiv.2504.14405},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/gahlot2025IMAGEsrp},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/gahlot2025IMAGEsrp/IMAGE_Abhinav.pdf},
	author = {Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@conference {deng2025IMAGEtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, IMAGE, image generative model, Imaging, Inverse problems, JRM, SEG, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/deng2025IMAGEtfi},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/deng2025IMAGEtfi/IMAGE_Venice.pdf},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {deng2025ML4SEISMICtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Reliable management of underground energy storage systems, such as underground compressed Hydrogene or Air storage, rely on semi-continuous monitoring and rigorous quantification of subsurface uncertainty. Time-lapse seismic imaging provides valuable insights into fluid migration and storage integrity. However, traditional inversion techniques{\textemdash}particularly full-waveform inversion (FWI){\textemdash}are inherently ill-posed and deterministic, limiting their applicability for uncertainty-aware decision-making. The Joint Recovery Method (JRM) improves consistency across multiple surveys by jointly reconstructing shared structures, but does not capture uncertainty. We introduce the Probabilistic Joint Recovery Method (πJRM), which embeds uncertainty estimation within a joint inversion framework. πJRM leverages a shared generative model to infer wave-equation{\textendash}based posterior distributions for each monitoring survey, enabling probabilistic interpretation of time-lapse changes. Synthetic experiments demonstrate that πJRM accurately recovers dynamic reservoir evolution and provides uncertainty-aware reconstructions, offering a principled foundation for safe, efficient management of subsurface energy storage operations.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, image generative model, Imaging, Inverse problems, jrm pjrm, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/deng2025ML4SEISMICtfi},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@article {gahlot2024uads,
	title = {An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring},
	journal = {Geophysical Journal International},
	year = {2025},
	note = {(Geophysical Journal International)},
	month = {5},
	abstract = {As a society, we are faced with important challenges to combat climate change. Geological Carbon Storage (GCS), during which gigatonnes of super-critical CO2 are stored underground, is arguably the only scalable net-negative CO2-emission technology that is available. While promising, subsurface complexities and heterogeneity of reservoir properties demand a systematic approach to quantify uncertainty when optimizing production and mitigating storage risks, which include assurances of Containment and Conformance of injected supercritical CO2. As a first step towards the design and implementation of a Digital Twin for monitoring and control of underground storage operations, a machine-learning-based data-assimilation framework is introduced and validated on carefully designed realistic numerical simulations. Because our implementation is based on Bayesian inference, but does not yet support control and decision-making, we coin our approach an uncertainty-aware Digital Shadow. To characterize the posterior distribution for the state of CO2 plumes (its CO2 concentration and pressure), conditioned on multi-modal time-lapse data, the envisioned Shadow combines techniques from Simulation-Based Inference (SBI) and Ensemble Bayesian Filtering to establish probabilistic baselines and assimilate multi-modal data for GCS problems that are challenged by large degrees of freedom, nonlinear multiphysics, non-Gaussianity, and computationally expensive to evaluate fluid-flow and seismic simulations. To enable SBI for dynamic systems, a recursive scheme is proposed where the Digital Shadow{\textquoteright}s neural networks are trained on simulated ensembles for their state and observed data (well and/or seismic). Once training is completed, the system{\textquoteright}s state is inferred when time-lapse field data becomes available. Contrary to ensemble Kalman filtering, corrections to the predicted simulated states are not based on linear updates, but instead follow during the Analysis step of Bayesian filtering from a prior-to-posterior mapping through the latent space of a nonlinear transform. Starting from a probabilistic model for the permeability field, derived from a baseline surface-seismic survey, the proposed Digital Shadow is validated on unseen simulated ground-truth time-lapse data. In this computational study, we observe that a lack of knowledge on the permeability field can be factored into the Digital Shadow{\textquoteright}s uncertainty quantification. Our results also indicate that the highest reconstruction quality is achieved when the state of the CO2 plume is conditioned on both time-lapse seismic data and wellbore measurements. Despite the incomplete knowledge of the permeability field, the proposed Digital Shadow was able to accurately track the unseen physical state of the subsurface throughout the duration of a realistic CO2 injection project. To the best of our knowledge, this work represents the first proof-of-concept of an uncertainty-aware, in-principle scalable, Digital Shadow that captures the uncertainty arising from unknown reservoir properties and noisy observations. This framework provides a foundation for the development of a Digital Twin aimed at mitigating risks and optimizing the management of underground storage projects.},
	keywords = {Bayesian inference, conditional normalizing flows, deep learning, digital twin, GCS, Inverse problems, sequential Bayes, Summary Statistics, Uncertainty quantification},
	doi = {10.1093/gji/ggaf176},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2024/gahlot2024uads/paper.html},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {orozco2024IMAGEbeacon,
	title = {BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows - a case study in optimal monitor well placement for CO2 sequestration},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {CO2 sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO2 plumes to prevent risks such as leakage, induced seismicity, or breaching licensed boundaries. To address this, project managers use borehole wells for direct CO2 and pressure monitoring at specific locations. Given the high costs associated with drilling, it is crucial to strategically place a limited number of wells to ensure maximally effective monitoring within budgetary constraints. Our approach for selecting well locations integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks for plume inference uncertainty. Our methodology is extensible to three-dimensional domains and is developed within a Bayesian framework for optimal experimental design, ensuring scalability and mathematical optimality. We use a realistic case study to verify these claims by demonstrating our method{\textquoteright}s application in a large scale domains and optimal performance as compared to baseline well placement.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, deep learning, GCS, IMAGE, Inverse problems, OED, SEG, Uncertainty quantification},
	doi = {10.48550/arXiv.2404.00075},
	author = {Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {gahlot2024IMAGEdt,
	title = {A Digital Twin for Geological Carbon Storage with Controlled Injectivity},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO2 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 CO2 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., CO2 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 CO2 storage operations.},
	keywords = {Bayesian inference, conditional normalizing flows, control, deep learning, digital twin, GCS, IMAGE, SEG, sequential Bayes, Uncertainty quantification},
	doi = {10.48550/arXiv.2403.19819},
	url = {https://slimgroup.github.io/IMAGE2024/GahlotLi2024SEG/paper.html},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@presentation {gahlot2024ICLdtg,
	title = {Digital Twins in the era of generative AI - Application to Geological CO2 Storage},
	year = {2024},
	month = {6},
	abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines "A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring \& control of geological CO2 storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, control, data assimilation, deep learning, digital twin, FWI, GCS, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICL/2024/gahlot2024ICLdtg},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {herrmann2024PURDUEicon,
	title = {Digital Twins in the era of generative AI {\textemdash} Application to Geological CO2 Storage},
	booktitle = {ICON Seminar in IoT},
	year = {2024},
	month = {9},
	abstract = {As a society, we are faced with important challenges to combat climate change. Geological Carbon Storage, during which gigatonnes of super-critical CO2 are stored underground, is arguably the only scalable net-negative CO2-emission technology that is available. Recent advances in generative AI offer unique opportunities{\textemdash}especially in the context of Digital Twins for subsurface CO2-storage monitoring, decision making, and control{\textemdash}to help scale this technology, optimize its operations, lower its costs, and reduce its risks, so assurances can be made whether storage projects proceed as expected and whether CO2 remains underground. During this talk, it is shown how techniques from Simulation-Based Inference and Ensemble Bayesian Filtering can be extended to establish probabilistic baselines and assimilate multimodal data for problems challenged by large degrees of freedom, nonlinear multiphysics, and computationally expensive to evaluate simulations. Key concepts that will be reviewed include neural Wave-Based Inference with Amortized Uncertainty Quantification and physics-based Summary Statistics, Ensemble Bayesian Filtering with Conditional Neural Networks, and learned multiphysics inversion with Differentiable Programming.},
	keywords = {Bayesian inference, conditional normalizing flows, differential programming, digital twin, Ensemble Bayesian filtering, GCS, multiphyics, sequential Bayes, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/PURDUEicon/2024/herrmann2024PURDUEicon},
	url2 = {https://www.youtube.com/watch?v=GQT9CLA-DlU},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {gahlot2024MINESGIGACO2dtg,
	title = {DT4GCS {\textemdash} Digital Twin for Geological CO2 Storage and Control},
	booktitle = {Geophysical Research for Gigatonnes CO2 Storage, Colorado School of Mines},
	year = {2024},
	month = {7},
	keywords = {Bayesian inference, conditional normalizing flows, control, deep learning, digital twin, GCS, sequential Bayes, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/MINESGIGACO2/2024/gahlot2024MINESGIGACO2dtg},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/MINESGIGACO2/2024/gahlot2024MINESGIGACO2dtg/DTCCUS24.docx},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {zeng2024ML4SEISMICepu,
	title = {Enhancing Performance with Uncertainty Estimation in Geological Carbon Storage Leakage Detection from Time-Lapse Seismic Data},
	year = {2024},
	month = {11},
	abstract = {Ensuring CO 2  non-leakage is a critical aspect of Geological Carbon Storage (GCS). While previous approaches that develop deep neural networks demonstrate promising automatic leakage detection and potential cost reduction in dataset collection from time-lapse seismic images, they face challenges, such as a limited ability to reduce false alarms in CO 2  leakage instances and a lack of uncertainty analysis in detection results. This paper introduced a framework aimed at enhancing the deep neural network model{\textquoteright}s ability to detect GCS leakage risk through a multi-criteria decision-making (MCDM)-based ensemble algorithm. The proposed method can improve the detection ability of leakage cases while accurately distinguishing them from non-leakage instances. Furthermore, the proposed uncertainty analysis method utilizing Monte Carlo (MC) dropout technique efficiently identifies misclassified non-leakage cases and categorizes them as undetermined for further investigation. This comprehensive approach enhances both the reliability and performance of the model in detecting GCS leakage risks.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICepu},
	author = {Shiqin Zeng and Huseyin Tuna Erdinc and Ziyi Yin and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {herrmann2024SIAMUQdt,
	title = {An Uncertainty-Aware Digital Twin for Geological Carbon Storage},
	booktitle = {SIAM Conference on Uncertainty Quantification},
	year = {2024},
	note = {(SIAM UQ, Trieste)},
	month = {03},
	abstract = {Arguably, Geological Carbon Storage constitutes the only truly scalable net-negative carbon emission technology. To mitigate its risks and optimize its operations, an uncertainty-aware Digital Twin is being developed. To leverage existing fluid-flow and seismic simulation and imaging capabilities, the envisioned twin combines techniques from sequential and simulation-based Bayesian inference to train its deep generative neural networks to draw samples from the posterior of the Digital Twin{\textquoteright}s state. Because these samples are conditioned on observed time-lapse field data, these twins are capable of capturing the dynamics of CO2 plumes and their uncertainty.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, GCS, Imaging, monitoring, SIAM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/digital-twin},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Grant Bruer and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {erdinc2022ML4SEISMICdgp,
	title = {De-risking GCS projects with explainable CO$_2$ leakage detection in time-lapse seismic images},
	year = {2022},
	month = {11},
	abstract = {With the global deployment of Carbon, capture and storage (CCS) technology to combat climate change, there is an associated risk of contamination with CO2 leaking back to the atmosphere. Thus, it requires continuous monitoring of CO2 after the injection stops at the storage site. In this work, we generated synthetic CO2 plume development data with both leakage and no leakage scenarios. We trained a convolutional neural network (CNN) discriminative classifier and also a generative classifier and compared their performances in CO2 leakage detection. The accuracy of our discriminative classifier on the test data is 85\% and that of the generative classifier is 90\%. The Class Activation Mapping (CAM) results of the discriminative classifier and the latent space representation of our dataset in the case of generative classifier strengthens our claims about trustworthy leakage classification.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, JRM, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/erdinc2022ML4SEISMICdgp/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICsfg,
	title = {Simulation-based framework for geological carbon storage monitoring},
	year = {2022},
	month = {11},
	abstract = {While various monitoring modalities exist to track the behavior of CO2 plumes to ensure safe operations and compliance with regulatory requirements, active 3D time-lapse seismic monitoring has proven superior but costly. At SLIM, we aim to reduce the operating costs by optimizing acquisition design, to help drive innovations in seismic monitoring acquisition design and imaging, and to test novel time-lapse acquisition and imaging technologies in silico at scale. In this talk, we will introduce our open-source software platform simulation-based monitoring design framework. We demonstrate how to make use of proxy models for seismic properties derived from real 3D imaged seismic and well data to conduct realistic synthetic geological carbon storage projects. Furthermore, we discuss our proposed sparse non-replicated seismic acquisition and cutting-edge methodology to recover the dense data or to directly image the sparse non-replicated via joint recovery model. This automatic workflow ends with deep neural classifiers to detect potential CO2 leakage over seal through pressure-induced fault openings. We envisage the development of an automatic workflow to handle the large number of continuously monitored CO2 injection sites needed to help combat climate change.},
	keywords = {CAM, CCS, classification, deep learning, explainability, GCS, Imaging, JRM, machine learning, ML4SEISMIC, SLIM, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICsfg/index.html},
	author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
