Geospatially Tuned AI for ROP Optimization: A Context-Aware Machine Learning Approach to Enhance Drilling Efficiency

Optimizing the Rate of Penetration (ROP) is critical for reducing drilling time and overall, well construction costs. Traditional ROP tuning relies on manual, experience-based methods that are prone to subjectivity and inefficiency. This paper presents an AI-powered solution that uses high-frequency drilling data and machine learning to systematically optimize ROP during the planning stage. The system is designed to provide contextualized recommendations based on geological relevance, formation type, and operational configuration across diverse well environments. By incorporating geospatial filtering, the approach ensures that optimizations are tailored to local analogs, enhancing accuracy in varied geological settings. The approach uses supervised machine learning models trained on historical drilling datasets, incorporating key operational parameters such as Weight on Bit (WOB), Rotary Speed (RPM), Flow Rate (MFI), and bit design. Advanced visualization techniques including Partial Dependency Plots (PDPs) and mesh grid analysis are used to identify optimal operational "sweet spots." The solution integrates filters for formation, BHA drive type, section size, and bit type to tailor results. A web-based interface enables spatial filtering using latitude and longitude, allowing engineers to restrict analysis to nearby wells and ensuring geologically relevant recommendations based on local analogs. The AI-based ROP optimization workflow was developed using high-frequency drilling data from Geoscience Australia's NOPIMS database. The model effectively identified optimal operational parameters such as WOB, RPM, and Flow Rate, generating interpretable outputs through tools like Partial Dependency Plots (PDPs) and mesh grid surfaces. A geospatial filtering capability enabled localized analysis by benchmarking wells based on proximity and geological similarity, increasing contextual relevance and accuracy. The model's intuitive, user-driven interface enables dynamic adjustment of parameters during planning and execution. By transforming historical data into actionable, localized insights, this AI workflow offers a scalable and geologically contextualized solution for ROP optimization. It establishes a foundation for broader real-time integration and future deployments across diverse drilling scenarios. This work introduces a unified platform that combines machine learning, geospatial intelligence, and explainable analytics for drilling optimization, marking a first-of-its-kind integration in ROP workflows. By using publicly available datasets from Geoscience Australia's NOPIMS platform, it demonstrates a transparent, globally scalable, and data-driven methodology. By incorporating geospatial intelligence, the solution ensures that recommendations are contextually relevant to the planned well's location and geological setting, representing a paradigm shift from experience-based decision-making to data-driven optimization.