Efficient machine-learning surrogates for large-scale geological carbon
  and energy storage
Teeratorn Kadeethum Stephen J. Verzi Hongkyu Yoon
Geological carbon and energy storage are pivotal for achieving net-zero
carbon emissions and addressing climate change. However, they face
uncertainties due to geological factors and operational limitations, resulting
in possibilities of induced seismic events or groundwater contamination. To
overcome these challenges, we propose a specialized machine-learning (ML) model
to manage extensive reservoir models efficiently.
  While ML approaches hold promise for geological carbon storage, the
substantial computational resources required for large-scale analysis are the
obstacle. We've developed a method to reduce the training cost for deep neural
operator models, using domain decomposition and a topology embedder to link
spatio-temporal points. This approach allows accurate predictions within the
model's domain, even for untrained data, enhancing ML efficiency for
large-scale geological storage applications.