Can machine learning improve carbon storage? Synergies of deep learning, uncertainty quantification and intelligent process control

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Authors

Thilo Wrona , Indranil Pan

Abstract

As we transition from fossil fuel to renewable energy, negative emission technologies, such ascarbon capture and storage (CCS), can help us reduce CO2 emissions. Effective CO2 storage requires: (1) detailed site characterization, (2) regular, integrated risk assessment, and (3) flexible design and operation. We believe that recent advances in machine learning coupled with uncertainty quantification and intelligent process control help us with these task and thus im-prove the efficiency and safety of subsurface CO2 storage.

DOI

https://doi.org/10.31223/X5XW61

Subjects

Earth Sciences, Geology, Geophysics and Seismology, Sustainability

Keywords

carbon capture and storage, machine learning, Deep learning, Uncertainty quantification, intelligent process control

Dates

Published: 2021-12-02 12:25

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

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