This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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 02:25
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None
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