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

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.