  We develop a data-driven model, introducing recent advances in machine
learning to reservoir simulation. We use a conventional reservoir modeling tool
to generate training set and a special ensemble of artificial neural networks
(ANNs) to build a predictive model. The ANN-based model allows to reproduce the
time dependence of fluids and pressure distribution within the computational
cells of the reservoir model. We compare the performance of the ANN-based model
with conventional reservoir modeling and illustrate that ANN-based model (1) is
able to capture all the output parameters of the conventional model with very
high accuracy and (2) demonstrate much higher computational performance. We
finally elaborate on further options for research and developments within the
area of reservoir modeling.
