  Reservoir simulations are computationally expensive in the well control and
well placement optimization. Generally, numerous simulation runs (realizations)
are needed in order to achieve the optimal well locations. In this paper, we
propose a graph neural network (GNN) framework to build a surrogate
feed-forward model which replaces simulation runs to accelerate the
optimization process. Our GNN framework includes an encoder, a process, and a
decoder which takes input from the processed graph data designed and generated
from the simulation raw data. We train the GNN model with 6000 samples
(equivalent to 40 well configurations) with each containing the previous step
state variable and the next step state variable. We test the GNN model with
another 6000 samples and after model tuning, both one-step prediction and
rollout prediction achieve a close match with the simulation results. Our GNN
framework shows great potential in the application of well-related subsurface
optimization including oil and gas as well as carbon capture sequestration
(CCS).
