Machine Learning-Based Prediction and Optimization of Mechanical Specific Energy in Drilling Operations: A Gas Field Case Study in Romania

Drilling optimization in complex geological environments requires efficient control of operational parameters and energy consumption. This study presents a data-driven workflow for Mechanical Specific Energy (MSE) prediction and optimization using Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR) models applied to drilling data from a gas field in southwestern Romania. Operational parameters, including weight on bit (WOB), rotary speed (RPM), flow rate, torque, and rate of penetration (ROP), were integrated to capture the nonlinear relationship between drilling conditions and MSE behavior. The results demonstrated that all developed models successfully predicted MSE under variable drilling conditions. Among the evaluated models, the Random Forest model achieved the highest prediction accuracy, while the ANN model showed strong generalization capability. Furthermore, an optimal operational window associated with reduced MSE, improved drilling efficiency, and stable drilling conditions was identified. The proposed workflow provides a practical framework for drilling optimization and supports the implementation of intelligent real-time drilling advisory systems for operational decision-making.