From Reactive to Proactive: Machine Learning-Powered Drilling Analytics for Operational Excellence

Abstract

Drilling operations face substantial challenges from non-productive time (NPT) and wellbore instability, costing the industry over $7 billion annually. Current optimization efforts are constrained by three key data challenges: (1) analytical complexity in processing historical data, (2) statistical limitations in pattern recognition, and (3) restricted access to relevant datasets. This study presents an integrated machine learning (ML) approach that transforms historical drilling data analysis from reactive troubleshooting to predictive risk management, enabling preemptive mitigation of instability events through data-driven decision making.

Traditional mitigation strategies are hampered by fragmented data systems, with nearly 40% of drilling data remaining underutilized (SPE, 2022), and reliance on reactive problem-solving. This study showcases how historical data sheds light on challenging formations, enabling proactive mitigation planning through exploring the statistical analysis of geomechanics studies, offset wells, and drilling parameters. The proposed methodology workflow adopts two-phase methodology to harness historical data for drilling optimization. First, a structured data integration process focusing on geomechanical parameters (e.g., WOB, RPM, mud weight, ROP, ECD, shale reactivity), stuck pipe incidents, and NPT logs; and Second, ML-driven predictive modeling employs ensemble machine learning methods (Gradient Boosting, Random Forest) versus linear regression trained on offset data to forecast instability risks.

The study outputs emphasize the critical role of structured data management in handling overwhelming historical datasets, focusing on specific drilling events to inform future well designs. Analyzing multiple sets of offset wells before new drilling projects, the study unveils strategies to address issues like stuck pipe due to wellbore instability, clay swelling, and NPT reduction from drilling operations in the Middle East and Asia. This study illuminates the optimization of diverse drilling scenarios, including gas wells, clay formations, and laminar shaly formations through the application of historical statistical geomechanics studies, showcasing the transformative role of machine learning and data analytics in enhancing drilling performance.

Key outcomes include: (1) validated strategies for mitigating stuck pipe incidents and clay swelling, (2) a real-time recommendation engine for dynamic parameter adjustment (WOB, RPM, mud weight), and (3) significant NPT reduction through structured data management. Field applications of this framework across high-risk wells demonstrated significant improvements in drilling efficiency and cost reduction. The synergy between geomechanics studies and historical data analysis, empowered by machine learning, emerges as a pivotal component. This holistic partnership forms a beacon navigating the complexities of drilling optimization, paving the way for operational excellence.

This study provides a roadmap for industry applications, showcasing the symbiotic relationship between geomechanics studies and ML-driven historical data analysis to enhance operational excellence and minimize NPT in drilling projects.