<![CDATA[The selection of an appropriate artificial lift method is critical in the oil and gas industry to ensure production continuity as reservoir pressure declines. However, current selection processes still largely rely on technical expertise and conventional heuristic approaches, which are often insufficient for handling the complexity of reservoir characteristics and dynamic operational conditions. This study aims to evaluate the performance of three tree-based machine learning algorithms—Decision Tree, Random Forest, and Gradient Boosting—in predicting the optimal artificial lift method. Historical field data, including fluid flow rate, temperature, API gravity, and artificial lift method labels, were used to train the models. The data underwent preprocessing steps such as data cleaning, encoding, and splitting into training and testing sets before being modeled using the Scikit-learn library. The performance of the three models was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm achieved an accuracy of approximately 81%, Random Forest yielded the highest accuracy at around 94% (with a validation accuracy of 93.67%), while Gradient Boosting performed the least effectively with an accuracy of about 64%. Feature importance and SHAP analysis revealed that temperature was the most influential variable in selecting the artificial lift method, followed by API gravity and fluid flow rate. In conclusion, Random Forest was the most effective model, offering the best combination of accuracy and stability in predicting the optimal artificial lift method.]]>
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