  Geological carbon storage entails the injection of megatonnes of
supercritical CO2 into subsurface formations. The properties of these
formations are usually highly uncertain, which makes design and optimization of
large-scale storage operations challenging. In this paper we introduce a
history matching strategy that enables the calibration of formation properties
based on early-time observations. Early-time assessments are essential to
assure the operation is performing as planned. Our framework involves two
fit-for-purpose deep learning surrogate models that provide predictions for
in-situ monitoring well data and interpreted time-lapse (4D) seismic saturation
data. These two types of data are at very different scales of resolution, so it
is appropriate to construct separate, specialized deep learning networks for
their prediction. This approach results in a workflow that is more
straightforward to design and more efficient to train than a single surrogate
that provides global high-fidelity predictions. The deep learning models are
integrated into a hierarchical Markov chain Monte Carlo (MCMC) history matching
procedure. History matching is performed on a synthetic case with and without
4D seismic data, which allows us to quantify the impact of 4D seismic on
uncertainty reduction. The use of both data types is shown to provide
substantial uncertainty reduction in key geomodel parameters and to enable
accurate predictions of CO2 plume dynamics. The overall history matching
framework developed in this study represents an efficient way to integrate
multiple data types and to assess the impact of each on uncertainty reduction
and performance predictions.
