@unpublished {gahlot2024IMAGEdt,
	title = {A Digital Twin for Geological Carbon Storage with Controlled Injectivity},
	year = {2024},
	month = {3},
	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, 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 {herrmann2024dt4gcs,
	title = {Digital Twins in the Era of Generative AI: Application to Geological CO 2  Storage},
	year = {2024},
	month = {2},
	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 CO 2  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 = {Bayesian inference, CCS, deep learning, digital twin, Imaging, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs.pdf},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@unpublished {herrmann2024CSMdt,
	title = {DT4GCS {\textendash}- Digital Twin for Geological CO2 Storage and Control},
	year = {2024},
	month = {2},
	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 {\textquotedblleft}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.{\textquotedblright} During this talk, we will explore these concepts and their significance in addressing the challenges of monitoring and 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 = {Bayesian inference, CCS, deep learning, digital twin, Imaging, monitoring, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2024/herrmann2024CSMdt/herrmann2024CSMdt.pdf},
	author = {Felix J. Herrmann and Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Haoyun Li}
}
@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 {herrmann2022ML4SEISMICmod,
	title = {Meet our digital twin for geological carbon storage},
	year = {2022},
	month = {11},
	abstract = {By embracing recent developments in simulation-based Bayesian inference{\textemdash}i.e., the task of deriving statistical information from a system based on in silico simulations{\textemdash}we envisage the development of an uncertainty-aware Digital Twin for seismic monitoring of Geologic Carbon Storage (GCS). According to IBM, {\textquotedblleft}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{\textquotedblright}. This Digital Twin will be designed to maximally benefit from vastly improved abilities to simulate complex phenomena, including the development of CO2 plumes in saline aquifers, and from the ability of neural networks to learn by example as part of inference. For GCS, this means that systematic assessment of uncertainties now becomes possible when observing CO2 plumes from time-lapse geophysical data (e.g., seismic). Because the proposed Digital Twin{\textquoteright}s neural networks are taught to produce samples from the probability distribution for the CO2 plume conditioned by the observed time-lapse data, this approach will provide access to this information on uncertainty. As part of ML4Seismic, we are working on various aspects regarding the development of the Digital Twin including: (i) capability to generate realistic time-lapse data in response to CO2 injection in large strongly heterogeneous reservoirs. This simulation framework will facilitate the design of high-fidelity monitoring systems and is unique since it uses proxy Earth models with realistic CO2 plumes and heterogeneity; (ii) An inversion framework capable of producing high-fidelity time-lapse images of CO2 plumes and reservoir properties from time-lapse data collected in response to CO2 injection; (iii) uncertainty-aware data-assimilation framework based on techniques from sequential Bayes and capable of rapidly producing high-fidelity CO2 plume forecasts that are consistent with observed time-lapse data; (iv) A scalable uncertainty-aware early warning system designed to safeguard CO2 injection operations built on the latest insights from interpretable and trustworthy (explainable and robust) machine learning. After describing how to build a Digital Twin for GSC, early results will be presented on the use of Fourier Neural Networks as surrogates for the two-phase flow equations, seismic monitoring with our joint recovery model, and the use of spectral ratio to design low-cost acquisitions for time-lapse seismic.},
	keywords = {CCS, digital twin, GCS, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/herrmann2022ML4SEISMICmod/index.html},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2022DIRACCudt,
	title = {Uncertainty-aware Digital Twin for Monitoring Geological Carbon Storage},
	year = {2022},
	note = {(Diracc inaugural meeting, Atlanta)},
	month = {11},
	keywords = {CAM, CCS, classification, digital twin, Diracc, explainability, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Diracc/2022/herrmann2022DIRACCudt/index.html},
	author = {Felix J. Herrmann}
}
