  Phase equilibrium calculations are an essential part of numerical simulations
of multi-component multi-phase flow in porous media, accounting for the largest
share of the computational time. In this work, we introduce a GPUenabled, fast,
and parallel framework, PTFlash, that vectorizes algorithms required for
isothermal two-phase flash calculations using PyTorch, and can facilitate a
wide range of downstream applications. In addition, to further accelerate
PTFlash, we design two task-specific neural networks, one for predicting the
stability of given mixtures and the other for providing estimates of the
distribution coefficients, which are trained offline and help shorten
computation time by sidestepping stability analysis and reducing the number of
iterations to reach convergence. The evaluation of PTFlash was conducted on
three case studies involving hydrocarbons, CO 2 and N 2 , for which the phase
equilibrium was tested over a large range of temperature, pressure and
composition conditions, using the Soave-Redlich-Kwong (SRK) equation of state.
We compare PTFlash with an in-house thermodynamic library, Carnot, written in
C++ and performing flash calculations one by one on CPU. Results show speed-ups
on large scale calculations up to two order of magnitudes, while maintaining
perfect precision with the reference solution provided by Carnot.
