Abstract: Methods and systems for hydrocarbon phase behavior modeling for compositional reservoir simulation, the methods and systems configured for estimating phase properties of a hydrocarbon sample based on a mole-fraction weighted mixing rule; determining contributions of individual phase components to the mole-fraction weighted phase properties; generating input data for a machine learning model including a first sub-network and a second sub-network, the input data including the contributions from the phase properties; generating, based on processing the input data using the first sub-network of the machine learning model, probability values for each potential phase state; processing the probability values and input data by the second sub-network of the machine learning model; and generating, by the second sub-network, output data including equilibrium K-values, vapor fraction, vapor compressibility, and liquid compressibility for the hydrocarbon sample.

Abstract: A method includes obtaining a sample of a fluid from a subterranean zone while the fluid is being extracted from the zone. A chemical composition of the sample is measured. A temperature and a pressure of the subterranean zone are measured. The measured properties are associated with a time point. The measured properties are incorporated into a set of historical data. A chemical composition of a fluid to be extracted from the subterranean zone at a future time point is determined based on the set of historical data. A presence of a liquid phase in the fluid to be extracted from the subterranean zone at the future time point is determined. A flow rate of the fluid being extracted from the subterranean zone is adjusted in response to determining the presence of the liquid phase in the fluid to be extracted from the subterranean zone at the future time point.

Abstract: A method includes obtaining a sample of a fluid from a subterranean zone while the fluid is being extracted from the zone. A chemical composition of the sample is measured. A temperature and a pressure of the subterranean zone are measured. The measured properties are associated with a time point. The measured properties are incorporated into a set of historical data. A chemical composition of a fluid to be extracted from the subterranean zone at a future time point is determined based on the set of historical data. A presence of a liquid phase in the fluid to be extracted from the subterranean zone at the future time point is determined. A flow rate of the fluid being extracted from the subterranean zone is adjusted in response to determining the presence of the liquid phase in the fluid to be extracted from the subterranean zone at the future time point.

Abstract: A method includes obtaining a sample of a fluid from a subterranean zone while the fluid is being extracted from the zone. A chemical composition of the sample is measured. A temperature and a pressure of the subterranean zone are measured. The measured properties are associated with a time point. The measured properties are incorporated into a set of historical data. A chemical composition of a fluid to be extracted from the subterranean zone at a future time point is determined based on the set of historical data. A presence of a liquid phase in the fluid to be extracted from the subterranean zone at the future time point is determined. A flow rate of the fluid being extracted from the subterranean zone is adjusted in response to determining the presence of the liquid phase in the fluid to be extracted from the subterranean zone at the future time point.

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: A method includes obtaining a sample of a fluid from a subterranean zone while the fluid is being extracted from the zone. A chemical composition of the sample is measured. A temperature and a pressure of the subterranean zone are measured. The measured properties are associated with a time point. The measured properties are incorporated into a set of historical data. A chemical composition of a fluid to be extracted from the subterranean zone at a future time point is determined based on the set of historical data. A presence of a liquid phase in the fluid to be extracted from the subterranean zone at the future time point is determined. A flow rate of the fluid being extracted from the subterranean zone is adjusted in response to determining the presence of the liquid phase in the fluid to be extracted from the subterranean zone at the future time point.

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: A method includes obtaining a sample of a fluid from a subterranean zone while the fluid is being extracted from the zone. A chemical composition of the sample is measured. A temperature and a pressure of the subterranean zone are measured. The measured properties are associated with a time point. The measured properties are incorporated into a set of historical data. A chemical composition of a fluid to be extracted from the subterranean zone at a future time point is determined based on the set of historical data. A presence of a liquid phase in the fluid to be extracted from the subterranean zone at the future time point is determined. A flow rate of the fluid being extracted from the subterranean zone is adjusted in response to determining the presence of the liquid phase in the fluid to be extracted from the subterranean zone at the future time point.