A machine learning approach to Intelligent Artificial lift method selection

Artificial lift (AL) methods are crucial for optimizing well performance and sustaining hydrocarbon production in oil and gas operations. Traditional AL selection relies on conventional methodologies and human expertise, which may be inadequate for handling complex reservoir dynamics and varying operating conditions. As the industry seeks more efficient, data-driven solutions, machine learning (ML) presents an opportunity to enhance AL selection. This study develops an ML-based stack framework of Random Forest (RF), Extreme Gradient Boosting (XGB) and Decision Tree (DT) to predict optimal AL methods. The models are trained and validated on a comprehensive dataset incorporating well particulars, production parameters, reservoir properties, and operational conditions. Performance evaluation demonstrates that the ML models achieve up to 95% accuracy in AL selection, significantly improving on traditional methods. The findings highlight the potential of ML-driven AL selection to enhance production efficiency, reduce operational costs, and optimize field performance. This study provides a foundation for integrating AI-based decision-making into artificial lift optimization, offering a more adaptive and precise approach to production engineering.