Application of neural networks for determining the parameters of PVT-models used in solving hydrodynamic modeling problems

The article presents an overview of the application of machine learning models for problems of modeling the phase state of hydrocarbon systems. Determination of the state of a hydrocarbon system is possible in the presence of nonhydrocarbon components when performing hydrodynamic calculations on multiphase models. Such calculations are in demand when solving problems of both forecasting and adapting models to actual development data. It is shown that when adjusting models to actual data, the accuracy of reproducing laboratory experiments is in some cases higher than when using equations of state. But in some cases, the use of neural networks turns out to be useless. For relatively “simple” fluid systems, an increase in the complexity of the tool used does not provide an increase in accuracy compared to simple regression models. Another problem is the lack of a working tool that would completely replace the use of equations of state to assess the state of a fluid model when performing hydrodynamic calculations.