PTFlash : A vectorized and parallel deep learning framework for two-phase flash calculation

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 fast and parallel framework, PTFlash, that vectorizes algorithms required for two-phase flash calculation using PyTorch, and can facilitate a wide range of downstream applications. Vectorization promotes parallelism and consequently leads to attractive hardware-agnostic acceleration. 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, CO2 and N2, 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 of up to two order of magnitude on large scale calculations, while maintaining perfect precision with the reference solution provided by Carnot.

Automated summary

This paper introduces a fast and parallel framework, PTFlash, which uses vectorized algorithms and neural networks to accelerate phase equilibrium calculations, greatly reducing computation time while maintaining high precision.