Data Science Augmented Solutions in Carbon Capture & Storage, Hydrogen Storage and CO2 EOR
Tuesday, 24 September
Room 217 - 219
Technical Session
This technical session brings together researchers at the forefront of Carbon Capture & Storage (CCS), leveraging the power of machine learning to accelerate progress without sacrificing quality. Forecasting and optimization of CCS, Hydrogen Storage and CO2 EOR are challenging tasks due to large geological uncertainty, complex physics, and long forecast period. Data driven solutions are the key enablers of fast performance prediction and optimization under large uncertainties. This session showcases recent advances in flow simulation surrogate modeling, reduced order modeling, physics informed machine learning, and hybrid approaches of data science and physics-based modeling applied in this rapidly growing technology domain.
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1400-1425 220757The U-Net Enhanced Graph Neural Network for Multiphase Flow Prediction: An Implication to Geological Carbon Sequestration
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1425-1450 220865Optimizing Hydrogen Storage In The Subsurface Using A Reservoir-simulation-based And Deep-learning-accelerated Optimization Method
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1450-1515 221057Physics Informed Machine Learning For Reservoir Connectivity Identification And Production Forecasting In CO2-EOR
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1545-1610 220842Prediction of Minimum Miscibility Pressure between CO2 and Crude Oil by Integrating Improved Grey Wolf Optimization into SVM Algorithm
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1610-1635 220752Physics Augmented Machine Learning Models For Determining Gas Solubility In Formation Brines For CCS And Gas Processing Applications
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1635-1700 220772Exploring Sparsity-Promoting Dynamic Mode Decomposition for Data-Driven Reduced Order Modeling of Geological CO2 Storage
