Abstract: Technologies related to training machine-learning-based surrogate models for phase equilibria calculations are disclosed. In one implementation, an equation of state (EOS) for each of one or more regions of a reservoir is determined based on results of one or more pressure, volume, or temperature (PVT) experiments conducted on samples of downhole fluids obtained from one or more regions of the reservoir. Compositions of the samples of the downhole fluids are determined and spatially mapped based on interpolations between the one or more regions of the reservoir. One or more PVT experiments are simulated for the spatially mapped compositions of the downhole fluids using the determined EOS to create a compositional database of the reservoir. One or more machine-learning algorithms are trained using the compositional database, and the trained one or more machine-learning algorithms are used to predict phase stability and perform flash calculations for compositional reservoir simulation.

Abstract: Technologies related to training machine-learning-based surrogate models for phase equilibria calculations are disclosed. In one implementation, an equation of state (EOS) for each of one or more regions of a reservoir is determined based on results of one or more pressure, volume, or temperature (PVT) experiments conducted on samples of downhole fluids obtained from one or more regions of the reservoir. Compositions of the samples of the downhole fluids are determined and spatially mapped based on interpolations between the one or more regions of the reservoir. One or more PVT experiments are simulated for the spatially mapped compositions of the downhole fluids using the determined EOS to create a compositional database of the reservoir. One or more machine-learning algorithms are trained using the compositional database, and the trained one or more machine-learning algorithms are used to predict phase stability and perform flash calculations for compositional reservoir simulation.