  We present a novel technique for assessing the dynamics of multiphase fluid
flow in the oil reservoir. We demonstrate an efficient workflow for handling
the 3D reservoir simulation data in a way which is orders of magnitude faster
than the conventional routine. The workflow (we call it "Metamodel") is based
on a projection of the system dynamics into a latent variable space, using
Variational Autoencoder model, where Recurrent Neural Network predicts the
dynamics. We show that being trained on multiple results of the conventional
reservoir modelling, the Metamodel does not compromise the accuracy of the
reservoir dynamics reconstruction in a significant way. It allows forecasting
not only the flow rates from the wells, but also the dynamics of pressure and
fluid saturations within the reservoir. The results open a new perspective in
the optimization of oilfield development as the scenario screening could be
accelerated sufficiently.
