Removing the performance bottleneck of pressure–temperature flash calculations during both the online and offline stages by using physics-informed neural networks

Abstract

Pressure–temperature (PT) flash calculations are a performance bottleneck of compositional-flow simulations. With the sparse grid surrogate, the computing burden of PT flash calculations is shifted from the online stage to the offline stage of the compositional-flow simulations, and a great acceleration is achieved. It is known that the data-driven neural network can also be a surrogate of PT flash calculations. However, flash calculations are carried out in the training stage, i.e., the offline stage, which means the computing burden of PT flash calculations still exists in the offline stage. With physics-informed neural networks, the two heavy-burden routines of PT flash calculations, the successive substitution technique and stability analysis, are not carried out in the offline stage, and therefore, the computing burden in the offline stage is removed. After training, the phase condition and the compositions are the output of the neural network. The numerical experiments demonstrate the correctness and the applicability of the work. To the best of our knowledge, this is the first work to remove the performance bottleneck of PT flash calculations during both the online and offline stages of compositional-flow simulations.