Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations

Mud loss during drilling operations poses a significant problem in the oil and gas industry due to its contributions to increased costs and operational risks. This study aims to develop a reliable predictive model for mud loss volume using machine learning techniques to improve drilling efficiency and reduce non-productive time. The dataset consists of 949 field records from Middle Eastern drilling sites, incorporating variables such as borehole diameter, drilling fluid viscosity, mud weight, solid content, and pressure differential. Initial data analysis included statistical evaluation, outlier detection using leverage diagnostics, and data normalization to ensure validity and consistency. A Gradient Boosting Machine (GBM) served as the core predictor, with its hyperparameters fine-tuned using four optimization strategies: Evolution Strategies (ES), Batch Bayesian Optimization (BBO), Bayesian Probability Improvement (BBI), and Gaussian Process Optimization (GPO). Model performance was evaluated using k-fold cross-validation, with metrics including R², mean squared error and average absolute relative error percentage. Results demonstrated that the GBM-BPI achieved the strongest test performance (R² = 0.926, MSE = 1208.77, AARE% = 26.73), outperforming other approaches in accuracy and stability. Feature importance assessed through SHAP analysis revealed that hole size, formation type, and pressure differential were the most influential variables, while solid content had minimal effect.