Data-driven selection of artificial lift systems using machine-learning algorithms: Lago Agrio field case study

Abstract

This study presents a data-driven workflow to optimize artificial lift system (ALS) selection for wells in the Oriente Basin, Ecuador. The goal is to support engineers in choosing the most suitable ALS based on production and operational characteristics. Proper ALS selection is critical to maintain stable production, reduce unnecessary energy use, and minimize failures caused by mismatches between reservoir conditions and lift mechanisms. Historical well and production data were compiled and processed through data cleaning, feature engineering, and class-balancing techniques to improve representation of underused ALS categories. Multiple multiclass machine-learning classifiers were trained to predict the recommended ALS using key operational parameters. The best model was embedded in a web-based application that allows users to input well data and obtain data-driven ALS recommendations. Among the evaluated algorithms, XGBoost and a Stacking ensemble achieved the strongest performance, with test accuracies above 99%, while Random Forest and Decision Tree models reached about 96%. Overall evaluation shows high predictive capability. Comparison with field installations yielded an agreement of 83.3% between model recommendations and deployed ALSs. Although field choices do not always match model outputs, results indicate that the models provide robust and consistent predictions under current data conditions. The proposed workflow demonstrates that machine-learning–based ALS selection is a practical and reliable decision-support approach for wells with similar characteristics. Its use can help standardize selection criteria, reduce operational uncertainty, and improve production management efficiency across assets.