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CO2 Geological Storage Modeling with Machine Learning

Offered By: DataLearning@ICL via YouTube

Tags

Machine Learning Courses Computational Modeling Courses Numerical Simulations Courses Porous Media Courses Decarbonization Courses Multiphase Flow Courses

Course Description

Overview

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Explore a cutting-edge machine learning framework for modeling CO2 geological storage in this insightful talk by Gege Wen from Stanford University. Delve into the critical role of CO2 storage in global decarbonization efforts and the energy transition. Understand the challenges of traditional numerical simulations for predicting CO2 transport in subsurface formations, including high computational costs and scalability issues. Discover how this innovative machine learning approach offers several orders of magnitude speedup compared to conventional simulators while maintaining comparable accuracy. Learn how this framework enables real-time modeling to support engineering decisions and reduce uncertainties in CO2 storage deployment, potentially revolutionizing the evaluation of storage capacities and optimization of safe, effective injection sites.

Syllabus

Gege Wen - Stanford University - CO2 Geological Storage Modelling with Machine Learning


Taught by

DataLearning@ICL

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