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Petroleum & Petrochemical Engineering Journal




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, angle of inclination from horizontal (alpha), superficial liquid velocity, and the interfacial tension) as a “Liquid Loaded” or “Unloaded”. The second practice is to determine the critical gas velocity, and this is done by a regression method using the same inputs. Since the procedure is a data-driven approach, a considerable amount of data (247 well and lab measurements) collected from literatures has been used. Convenient ML technics have been applied from dividing the data to scaling, modeling and assessment. The results showed that a wellconstructed XGBoost model with an optimized hyperparameters is efficient in diagnosing the wells with the correct status and in predicting the onset of liquid loading by estimating the critical gas velocity. The assessment of the model was done relatively to existing correlations in literature. In the classification problem, the model showed a better performance with an F-1 score of 0.947 (correctly classified 46 cases from 50 used for testing). In contrast, the next best model was the one by Barnea with an F-1 score of 0.81 (correctly classified 37 from 50 cases). In the regression problem, the model showed an R2 of 0.959. In contrast, the second best model was the one by Shekhar with an R2 of 0.84. The results shown here prove that the model and the procedure developed give better results in diagnosing the well correctly if properly used by engineers.







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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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