METHOD FOR COST EFFECTIVE THERMO-DYNAMIC FLUID PROPERTY PREDICTIONS USING MACHINE-LEARNING BASED MODELS

摘要

A method for determining isothermal phase behavior for reservoir simulation includes generating a training data set using negative flash calculations, training a first machine learning algorithm to identify a supercritical phase and a subcritical phase, training a second machine learning algorithm to identify a number of stable phases in the subcritical phase, and training a third machine learning algorithm to determine a phase split of the subcritical phase that has more than one identified stable phase.