PTFlash : A deep learning framework for isothermal two-phase equilibrium calculations

TL;DR

PTFlash is a GPU-accelerated deep learning framework that significantly speeds up isothermal two-phase equilibrium calculations in multi-component flow simulations, maintaining high accuracy.

Contribution

It introduces a novel GPU-enabled, parallel framework with neural networks to predict stability and distribution coefficients, reducing computation time in phase equilibrium calculations.

Findings

Achieves up to 100x speed-up over traditional CPU methods.

Maintains perfect precision compared to reference solutions.

Effective across hydrocarbons, CO2, and N2 cases.

Abstract

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.