  Fast assimilation of monitoring data to update forecasts of pressure buildup
and carbon dioxide (CO2) plume migration under geologic uncertainties is a
challenging problem in geologic carbon storage. The high computational cost of
data assimilation with a high-dimensional parameter space impedes fast
decision-making for commercial-scale reservoir management. We propose to
leverage physical understandings of porous medium flow behavior with deep
learning techniques to develop a fast history matching-reservoir response
forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation
framework, the workflow updates geologic properties and predicts reservoir
performance with quantified uncertainty from pressure history and CO2 plumes
interpreted through seismic inversion. As the most computationally expensive
component in such a workflow is reservoir simulation, we developed surrogate
models to predict dynamic pressure and CO2 plume extents under multi-well
injection. The surrogate models employ deep convolutional neural networks,
specifically, a wide residual network and a residual U-Net. The workflow is
validated against a flat three-dimensional reservoir model representative of a
clastic shelf depositional environment. Intelligent treatments are applied to
bridge between quantities in a true-3D reservoir model and those in a
single-layer reservoir model. The workflow can complete history matching and
reservoir forecasting with uncertainty quantification in less than one hour on
a mainstream personal workstation.
