  Phase equilibrium calculation, also known as flash calculation, has been
extensively applied in petroleum engineering, not only as a standalone
application for separation process but also an integral component of
compositional reservoir simulation. It is of vital importance to accelerate
flash calculation without much compromise in accuracy and reliability, turning
it into an active research topic in the last two decades. In this study, we
establish a deep neural network model to approximate the iterative NVT-flash
calculation. A dynamic model designed for NVT flash problems is iteratively
solved to produce data for training the neural network. In order to test the
model's capacity to handle complex fluid mixtures, three real reservoir fluids
are investigated, including one Bakken oil and two Eagle Ford oils. Compared to
previous studies that follow the conventional flash framework in which
stability testing precedes phase splitting calculation, we incorporate
stability test and phase split calculation together and accomplish both two
steps by a single deep learning model. The trained model is able to identify
the single vapor, single liquid and vapor-liquid state under the subcritical
region of the investigated fluids. A number of examples are presented to show
the accuracy and efficiency of the proposed deep neural network. It is found
that the trained model makes predictions at most 244 times faster than the
iterative flash calculation under the given cases. Even though training a
multi-level network model does take a large amount of time that is comparable
to the computational time of flash calculations, the one-time offline training
process gives the deep learning model great potential to speed up compositional
reservoir simulation.
