In this work, we present the development of a compositional simulator accelerated by proxy flash calculation. We aim to speed up the compositional modeling of unconventional formations by stochastic training.
We first developed a standalone vapor-liquid flash calculation module with the consideration of capillary pressure and shift of critical properties induced by confinement. We then developed a fully connected network with 3 hidden layers using Keras. The network is trained with Adam optimizer. 250,000 samples are used as training data, while 50,000 samples are used as testing data. Based on the trained network, we developed a forward modeling (prediction) module in a compositional simulator. Therefore, during the simulation run, the phase behavior of the multicomponent system within each grid block at each iteration is obtained by simple interpolation from the forward module.
Our standalone flash calculation module matches molecular simulation results well. The accuracy of the trained network is up to 97%. With the implementation of the proxy flash calculation module, the CPU time is reduced by more than 30%. In the compositional simulator, less than 2% of CPU time is spent in the proxy flash calculation.
The novelty of this work lies in two aspects. We have incorporated the impacts of both capillary pressure and shift of critical properties in the flash calculation, which matches molecular simulation results well. We developed a proxy flash calculation module and implemented it in a compositional simulator to replace the traditional flash calculation module, speeding the simulation by 30%.