@presentation {li2023ML4SEISMICmci,
	title = {Maximizing CO2 injectivity within fracture pressure},
	year = {2023},
	month = {11},
	abstract = {In geological carbon storage projects, optimizing CO2 injection strategies is paramount to enhance storage efficiency and prevent leakage. The objective is to maximize the CO2 injection volume without surpassing the fracture pressure. Traditional adjoint-based approaches necessitate extensive numerical simulations, leading to significant computational overhead. To circumvent this challenge, we introduce an optimization framework based on physics-informed deep convolutional neural networks. Trained on different permeability slices, our model can rapidly predict the maximal CO2 injection volume for new permeability fields in real-time.},
	keywords = {CCS, ML4SEISMIC, Optimization, reservoir simulation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/li2023ML4SEISMICmci},
	author = {Haoyun Li and Ziyi Yin and Olav M{\o}yner and Felix J. Herrmann}
}
@article {grady2022SCtll,
	title = {Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs},
	journal = {Computers \& Geosciences},
	volume = {178},
	year = {2023},
	note = {(Computers \& Geosciences)},
	month = {07},
	pages = {105402},
	abstract = {Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches. Once trained, FNOs can achieve speed-ups of multiple orders of magnitude over conventional numerical PDE solvers. However, due to the high dimensionality of their input data and network weights, FNOs have so far only been applied to two-dimensional or small three-dimensional problems. To remove this limited problem-size barrier, we propose a model-parallel version of FNOs based on domain-decomposition of both the input data and network weights. We demonstrate that our model-parallel FNO is able to predict time-varying PDE solutions of over 2.6 billion variables on Perlmutter using up to 512 A100 GPUs and show an example of training a distributed FNO on the Azure cloud for simulating multiphase CO2 dynamics in the Earth{\textquoteright}s subsurface.},
	keywords = {CCS, Computers and Geosciences, deep learning, Fourier neural operators, HPC, large-scale, Model Parallelism, Multiphase Flow, Operator Learning},
	doi = {10.1016/j.cageo.2023.105402},
	url = {https://slim.gatech.edu/Publications/Public/Journals/ComputersAndGeosciences/2023/grady2022SCtll/grady2022SCtll.pdf},
	software = {https://github.com/slimgroup/dfno},
	author = {Thomas J. Grady II and Rishi Khan and Mathias Louboutin and Ziyi Yin and Philipp A. Witte and Ranveer Chandra and Russell J. Hewett and Felix J. Herrmann}
}
@presentation {herrmann2023ML4SEISMICmsc,
	title = {Monitoring subsurface CO2 plumes with learned sequential Bayesian inference},
	year = {2023},
	month = {11},
	abstract = {Reservoir engineers frequently employ two-phase flow simulations and history-matching to oversee and anticipate the behavior of CO2 plumes within geological carbon storage. These simulations, while valuable for gaining insights, face limitations due to several complex factors, such as uncertainties surrounding the plume{\textquoteright}s dynamics. To investigate this phenomenon more comprehensively, we introduce the concept of stochasticity in the dynamics, accounting for uncertainties in the underlying permeability of the reservoir. To enhance the accuracy of CO2 plume predictions and quantify the uncertainties involved, we utilize machine learning techniques to condition these predictions on time-lapse seismic and well observations. This framework works on the principle of sequential Bayesian inference that continuously assimilates information from time-lapse observations, updates the CO2 plume predictions, and characterizes uncertainties about the plumes.},
	keywords = {Bayesian inference, CCS, deep learning, Imaging, ML4SEISMIC, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/herrmann2023ML4SEISMICmsc},
	author = {Abhinav Prakash Gahlot and Ting-ying Yu and Rafael Orozco and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yu2023IMAGEmsc,
	title = {Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {To monitor and predict CO2 plume dynamics during geological carbon storage, reservoir engineers usually perform two-phase flow simulations. While these simulations may provide useful insights, their usefulness is limited due to numerous complicating factors including uncertainty in the dynamics of the plume itself. To study this phenomenon, we consider stochasticity in the dynamic caused by unknown random changes in the injection rate. By conditioning the CO2 plume predictions on seismic observations, we correct the CO2 plume predictions and quantify uncertainty with machine learning.},
	keywords = {Bayesian inference, CCS, deep learning, Imaging, monitoring, SEG, Uncertainty quantification},
	url = {https://slimgroup.github.io/IMAGE2023/SequentialBayes/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/yu2023IMAGEmsc},
	author = {Ting-ying Yu and Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2022ML4SEISMICmos,
	title = {ML4Seismic open-source software: updates and developments},
	year = {2022},
	month = {11},
	abstract = {Software is at the core of research and development in inverse problems. At SLIM, we have experience developing scalable and performant software, such as our legacy parallel MATLAB framework. With ML4Seismic, we are dedicated to build on this experience to develop HPC open source software (OSS) for the scientific community in collaboration with our partners. In this talk, we will describe our OSS Julia and Python environment, our high-level abstraction principles, and the range of solutions we offer for seismic processing and inversion and for machine learning. We will emphasize our aim to provide scalable software that can be easily applied to industrial problems.},
	keywords = {ML4SEISMIC, open-source, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMICmos/index.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Felix J. Herrmann}
}
@presentation {yu2022ML4SEISMICmsb,
	title = {Monitoring with sequential Bayesian inference},
	year = {2022},
	month = {11},
	abstract = {For this study, we apply sequential Bayesian inference to monitor the time evolution of subsurface flow of CO2 from indirect acoustic measurements at the surface. Upon receiving new acoustic measurements, we infer the current state of the CO2 by sampling from a learned posterior. Using the incoming data, we then perform online updates of the current posterior. This is accomplished by using the fluid flow model to advance the estimated state variable forward in time in order to update the learned posterior. With a synthetic experiment, we demonstrate this method can track the flow evolution accurately as measured by PSNR metrics. Since the posterior is a learned network, we can compute estimates faster than traditional least squares methods. This method can also quantify the uncertainty due to stochasticity in fluid flow model and the limited-azimuth imaging configuration.},
	keywords = {CCS, conditional normalizing flows, GCS, ML4SEISMIC, monitoring, sequential Bayes, SLIM, time-lapse, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yu2022ML4SEISMICmsb/index.html},
	author = {Ting-ying Yu and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
