Artificial neural network assisted two-phase flash calculations in isothermal and thermal compositional simulations

Abstract

Flash calculations are widely employed in compositional simulations in the determination of a number of phases, phase amount and phase composition at each grid block. The efficiency and robustness of a flash algorithm is critical to saving simulation run time and producing reliable simulation results. Traditional flash calculation methods that consist of a stability test and phase splitting calculations often require tremendous computational time to iteratively solve nonlinear equations. In general, the stability test is the most time-consuming part as it aims at calculating the compositions of an unstable trial phase from various empirical initial estimates. For the phase splitting calculations, their convergence behavior highly depends on the quality of initial guesses of K-values. Good initial guesses can significantly reduce the number of iterations, avoid converging to an incorrect solution and, therefore, enhance the efficiency and robustness of a flash algorithm. Conventionally, initial guesses for the phase splitting calculations come from the values at the previous time step, the neighboring grid blocks or the stability test, but sometimes they do not work well.In this work, we use artificial neural networks (ANNs) to assist the traditional flash calculations in achieving their fast and robust convergence. ANN models are built standalone prior to a simulation and can be considered as a preparation stage. Once the ANN models are successfully built, they can benefit a large number of simulation runs during history matching and production optimization. There are two ANN models developed in this work: an ANN-STAB model and an ANN-SPLIT model. The ANN-STAB model is built to fit a saturation pressure diagram and the ANN-SPLIT model is generated to regress mole fractions and K-values. At given pressure, temperature and feed composition, the ANN-STAB model is first used to label the stability of a system; if it shows instability, the ANN-SPLIT model is then used to predict the mole fractions and K-values, which are to be used as initial guesses in the following phase splitting calculations. Compared with the traditional stability test algorithms, the ANN-STAB model gives almost the same accuracy but consumes much less computational time. The ANN-SPLIT model provides thoroughly reliable initial guesses for phase splitting calculations. As a result, it requires far fewer Newton iterations.