Accurate intelligent modeling of mud loss while drilling wells via soft computing methods

Accurate prediction of mud loss volume in drilling operations is a critical challenge in industries such as petroleum engineering and geothermal well construction. Unforeseen mud loss leads to substantial economic losses, operational delays, and environmental concerns, underscoring the urgent need for robust predictive models. To address this, current study investigates the application of advanced ensemble machine learning techniques. Specifically, this study presents models utilizing Random Forest (RF), Adaptive Boosting (AdaBoost), and Decision Trees (DT), alongside a custom stacking-based Ensemble Learning framework that integrates Random Forest, AdaBoost, and Decision Tree models, to model and predict mud loss volume. The analyses incorporated key influencing factors: mud viscosity, differential pressure, hole size, and solid content. Empirical data comprising 2,820 observations were utilized from a Middle Eastern oil field. Data integrity was ensured through the leverage technique for outlier detection, and model generalizability was enhanced using 5-fold cross-validation during the training phase (90% of the dataset), with 10% reserved for independent testing. Among the evaluated models, the AdaBoost approach demonstrated superior predictive performance. It achieved a test coefficient of determination (R 2 ) of 0.828, on the testing dataset. Sensitivity analyses revealed that mud viscosity and solid content inversely affect mud loss, while hole size and differential pressure consistently lead to its increase. These results confirm the efficacy of AdaBoost for highly accurate mud loss prediction. This work distinguishes itself by providing a comprehensive comparison of multiple advanced ensemble ML techniques on a large, real-world dataset from an active oil field. The findings offer a more reliable and robust tool for forecasting mud loss, thereby enhancing operational efficiency and risk mitigation in drilling operations. This contributes to optimizing drilling decisions beyond the capabilities of traditional analytical methods by providing data-driven, actionable insights.