Machine Learning-Based Models for the Compressibility Factor of Natural Gas

Olga Kochueva, Ruslan Akhmetzianov
The paper presents an example of the so-called surrogate modeling. This is a computer modeling technique where machine learning methods are used to build a fast (surrogate) model that allows you to get a result with acceptable accuracy on data from a complex (physically proven, built on a solution of systems of nonlinear algebraic equations or partial differential equations) and resource-intensive model of an object or process. We develop and analyze the models for calculating the compressibility factor trained on a large amount of data calculated with AGA-8 equation of state. The presented models can be applied to replace the original model when analyzing the development of risk situations and searching for optimal gas transportation modes, in software designed for staff training on computer simulators.