Acceleration of the NVT-flash calculation for multicomponent mixtures using deep neural network models

Abstract

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.