Evaluation of Liquid Loading in Gas Wells Using Machine Learning

The inevitable result that gas wells witness during their life production is the liquid loading problem. The liquids that come with gas block the production tubing if the gas velocity supplied by the reservoir pressure is not enough to carry them to surface. Researchers used different theories to solve the problem naming, droplet fallback theory, liquid film reversal theory, characteristic velocity, transient simulations, and others. While there is no definitive answer on what theory is the most valid or the one that performs the best in all cases. This paper comes to involve a different approach, a combination between physics-based modeling and statistical analysis of what is known as Machine Learning (ML). The authors used a refined ML algorithm named XGBoost (extreme gradient boosting) to develop a novel full procedure on how to diagnose the well with liquid loading issues and predict the critical gas velocity at which it starts to load if not loaded already. The novel procedure includes a combination of a classification problem where a well will be evaluated based on some completion and fluid properties (diameter, liquid density, gas density, liquid viscosity, gas viscosity, an