Drilling Optimization and ROP Prediction with Hybrid ML Models

Purpose: The complexity of drilling activities has been enhanced by deeper wells, the heterogeneous formations, and the need to provide cost-effective and time-saving hydrocarbon production. One of the most important parameters of drilling performance is rate of Penetration (ROP), which has a direct impact on the efficiency of operations, non-productive time (NPT), and costs. The traditional mechanistic and empirical ROP models that had been important in the past are not very useful in nonlinear interaction, dynamic drilling conditions, and heterogeneous lithologies. However, existing reviews lack a structured problem statement that clearly identifies the limitations of standalone ML and classical ROP models under dynamic drilling conditions and the need for hybrid frameworks that improve accuracy, robustness, and real-time applicability. This review addresses this gap by systematically analyzing hybrid ML approaches and their role in drilling optimization. Materials and Methods: Improved drilling optimization through machine learning (ML) methods, especially hybrid ML models, has redefined the future of drilling optimization, which unites the advantages of various predictive models to improve accuracy, strength, and generalization. This review is a synthesis of literature on hybrid ML applications in ROP prediction, which is divided into three categories: optimization-integrated, ensemble, soft computing, and physics-informed models. Their methodologies, data requirements, real-time integration, operational problems, and performance in comparison to standalone ML models are addressed in the paper. Findings: The essential restrictions, including data quality, computing aspects, and the problem of interpretability, are identified, and the future research direction is also outlined. The synthesis offers an organized scheme of comprehending the development of hybrid ML models in the drilling optimization and outlines opportunities of future progress within the limitations of technologies. Unique contribution to theory, practice and policy: Improved drilling optimization through machine learning (ML) methods, especially hybrid ML models, has redefined the future of drilling optimization, which unites the advantages of various predictive models to improve accuracy, strength, and generalization.