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Geological carbon and energy storage are pivotal for achieving net-zerocarbon emissions and addressing climate change. However, they faceuncertainties due to geological factors and operational limitations, resultingin possibilities of induced seismic events or groundwater contamination. Toovercome these challenges, we propose a specialized machine-learning (ML) modelto manage extensive reservoir models efficiently.  While ML approaches hold promise for geological carbon storage, thesubstantial computational resources required for large-scale analysis are theobstacle. We've developed a method to reduce the training cost for deep neuraloperator models, using domain decomposition and a topology embedder to linkspatio-temporal points. This approach allows accurate predictions within themodel's domain, even for untrained data, enhancing ML efficiency forlarge-scale geological storage applications.

Efficient machine-learning surrogates for large-scale geological carbon and energy storage