@conference {gahlot2025EAGEnfb,
	title = {Advancing Geological Carbon Storage Monitoring with 3D Digital Shadow Technology},
	booktitle = {EAGE Annual Conference Proceedings},
	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/Conferences/EAGE/2025/gahlot2025EAGEnfb},
	author = {Abhinav Prakash Gahlot and Rafael Orozco 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@unpublished {gahlot2025EAGEagc,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}
}
@unpublished {li2025IMAGEceo,
	title = {CO2 Enhanced Oil Recovery Through Digital Shadow Modeling},
	year = {2025},
	month = {3},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, EOR, experimental design, FWI, GCS, Imaging, Inverse problems, permeability, reservoir simulation, RTM, Summary Statistics, Uncertainty quantification, WISE},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2025/li2025IMAGEceo/IMAGE_Haoyun.pdf},
	author = {Haoyun Li and Abhinav Prakash Gahlot 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@unpublished {gahlot2025EAGEerd,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}
}
@unpublished {erdinc2025IMAGEpbi,
	title = {Power-scaled Bayesian inference with score-based generative models},
	year = {2025},
	month = {3},
	keywords = {Bayesian inference, deep learning, generative model, Imaging, Inverse problems, power-scaling, RTM, score, Summary Statistics, Uncertainty quantification, WISE},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2025/erdinc2025IMAGEpbi/IMAGE_Tuna.pdf},
	author = {Huseyin Tuna Erdinc and Yunlin Zeng 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}
}
@unpublished {park2025IMAGErdn,
	title = {A reduced-order derivative-informed neural operator for subsurface fluid-flow},
	year = {2025},
	month = {3},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, Inverse problems, permeability, reduced-order modeling, reservoir simulation, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2025/park2025IMAGErdn/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@unpublished {gahlot2025IMAGEsrp,
	title = {Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring},
	year = {2025},
	month = {3},
	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, Summary Statistics, Uncertainty quantification, WISE},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2025/gahlot2025IMAGEsrp/IMAGE_Abhinav.pdf},
	author = {Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@unpublished {deng2025IMAGEtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	year = {2025},
	month = {3},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, image generative model, Imaging, Inverse problems, JRM, Summary Statistics, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2025/deng2025IMAGEtfi/IMAGE_Venice.pdf},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot 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}
}
@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}
}
@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}
}
@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}
}
@presentation {gahlot2024ML4SEISMICosm,
	title = {Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling},
	year = {2024},
	month = {11},
	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 = {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/gahlot2024ML4SEISMICosm},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {deng2024ML4SEISMICpjr,
	title = {Probabilistic Joint Recovery Method for CO2 plume monitoring},
	year = {2024},
	month = {11},
	abstract = {Accurately predicting fluid flow patterns in Carbon Capture and Storage (CCS) is a challenging task, particularly due to uncertainties in CO 2  plume dynamics and reservoir properties. While previous deterministic methods such as the Joint Recovery Method (JRM) have provided valuable insights, their effectiveness is limited as tools for decision-making since they do not communicate uncertainty. To address this, we propose a Probabilistic Joint Recovery Method (PJRM) that computes the posterior distribution at each monitoring survey while leveraging the shared structure among surveys through a common generative model. By efficiently computing posterior distributions for each monitoring survey, this method aims to provide valuable uncertainty information to decision-makers in CCS projects, augmenting their workflow with principled risk minimization.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, igm, image generative model, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/deng2024ML4SEISMICpjr},
	author = {Zijun Deng and Rafael Orozco 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}
}
@unpublished {bruer2024smpd,
	title = {Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering},
	year = {2024},
	month = {9},
	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 {bruer2024ML4SEISMICsmp,
	title = {Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering},
	year = {2024},
	month = {11},
	abstract = {Monitoring CO 2  injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO 2  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 CO 2  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-CO 2  monitoring domain by applying this scalable DA algorithm to a high-dimensional CO 2  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 CO 2  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 CO 2  reservoir monitoring.},
	keywords = {Bayesian inference, data assimilation, ensemble Kalman filter, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bruer2024ML4SEISMICsmp},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Edmond Chow and Felix J. Herrmann}
}
@presentation {bhar2024ML4SEISMICshs,
	title = {Sensitivity of Horizontal shear waves on Geological Carbon Storage},
	year = {2024},
	month = {11},
	abstract = {With the growing focus on Carbon Capture and Storage (CCS) activities, the significance of shear wave velocity (Vs) in monitoring CO2 is becoming increasingly important. Shear waves are instrumental in detecting CO2 leakage, evaluating CCS-induced seismicity, and identifying caprock failure. According to Biot-Gassmann rock physics models, changes in seismic velocity are expected during CO2 injection. This work specifically focuses on the horizontal component of shear wave velocity (Vsh). Shear (Sh) waves are more sensitive to density variations than P-wave or Sv wave velocities, leading us to anticipate changes in Sh velocity as CO2 saturation increases. While P-wave velocity tends to decrease with supercritical CO2 injection, the investigation of its effects on Sh waves presents a particularly compelling area of study. Therefore, we simulated the wave equation incorporating both P-wave and Sh wave velocities to better understand their impacts on imaging and CO2 injection processes.},
	keywords = {FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SH wave, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bhar2024ML4SEISMICshs},
	author = {Ipsita Bhar and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@unpublished {gahlot2024uads,
	title = {An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring},
	year = {2024},
	month = {10},
	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 = {10.48550/arXiv.2410.01218},
	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}
}
