  Reservoir simulation and adaptation (also known as history matching) are
typically considered as separate problems. While a set of models are aimed at
the solution of the forward simulation problem assuming all initial geological
parameters are known, the other set of models adjust geological parameters
under the fixed forward simulation model to fit production data. This results
in many difficulties for both reservoir engineers and developers of new
efficient computation schemes. We present a unified approach to reservoir
simulation and adaptation problems. A single neural network model allows a
forward pass from initial geological parameters of the 3D reservoir model
through dynamic state variables to well's production rates and backward
gradient propagation to any model inputs and variables. The model fitting and
geological parameters adaptation both become the optimization problem over
specific parts of the same neural network model. Standard gradient-based
optimization schemes can be used to find the optimal solution. Using real-world
oilfield model and historical production rates we demonstrate that the
suggested approach allows reservoir simulation and history matching with a
benefit of several orders of magnitude simulation speed-up. Finally, to
propagate this research we open-source a Python-based framework DeepField that
allows standard processing of reservoir models and reproducing the approach
presented in this paper.
