Wang, Kun (University of Calgary) | Luo, Jia (University of Calgary) | Yan, Lin (Exploration and Development Research Institute, PetroChina) | Wei, Yizheng (Computer Modeling Group Ltd) | Wu, Keliu (China University of Petroleum) | Li, Jing (University of Calgary) | Chen, Fuli (Exploration and Development Research Institute, PetroChina) | Dong, Xiaohu (China University of Petroleum) | Chen, Zhangxin (University of Calgary)
EOS-based phase equilibrium calculations are usually used in compositional simulation to have accurate phase behaviour. Phase equilibrium calculations include two parts: phase stability tests and phase splitting calculations. Since the conventional methods for phase equilibrium calculations need to iteratively solve strongly nonlinear equations, the computational cost spent on the phase equilibrium calculations is huge, especially for the phase stability tests. In this work, we propose artificial neural network (ANN) models to accelerate the phase flash calculations in compositional simulations. For the phase stability tests, an ANN model is built to predict the saturation pressures at given temperature and compositions, and consequently the stability can be obtained by comparing the saturation pressure with the system pressure. The prediction accuracy is more than 99% according to our numerical results. For the phase splitting calculations, another ANN model is trained to provide initial guesses for the conventional methods. With these initial guesses, the nonlinear iterations can converge much faster. The numerical results show that 90% of the computation time spent on the phase flash calculations can be saved with the application of the ANN models.