Efficient machine-learning surrogates for large-scale geological carbon and energy storage
This addresses computational bottlenecks for researchers and engineers in geological storage, though it appears incremental as it builds on existing neural operator methods.
The paper tackles the high computational cost of machine-learning models for large-scale geological carbon and energy storage by developing a method to reduce training costs for deep neural operators, achieving accurate predictions even for untrained data.
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.