Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency

Abstract

Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.