Prediction of rate of penetration in oil and gas drilling: a comparative review of machine learning methods and future research directions

Abstract

Against the backdrop of continuously growing global energy demand, the rate of penetration (ROP) serves as a key indicator for measuring drilling efficiency. Accurate ROP prediction is significant for optimizing drilling parameters and reducing project costs. In recent years, machine learning (ML) has been widely applied in ROP prediction research due to its advantages in handling high-dimensional data and nonlinear modeling. This paper systematically reviews and comparatively analyses the research progress of machine learning in ROP prediction. Existing studies are classified into three categories: purely data-driven models, hybrid models integrating physical mechanisms, and intelligent optimisation and decision-support methods. Cross-study comparisons reveal that pure data-driven models generally achieve high prediction accuracy when sufficient data is available. However, multiple studies note their limitations in generalization capability and physical consistency under complex geological conditions. Hybrid models integrating physical mechanisms demonstrate better robustness and interpretability, but their higher computational complexity constrains potential for real-time applications. Intelligent optimization and decision support methods show promise in multi-objective collaborative optimization, though challenges in stability and real-time performance remain. Based on this review, four main research directions are summarized: deep integration of physical constraints and intelligent optimization algorithms, development of decision support systems for real-time drilling, advancement of interdisciplinary hybrid modeling methods, and application of efficient computing and edge intelligence technologies. These directions stem from recurring technical bottlenecks and research trends in recent literature, providing guidance for future research and engineering practice.