Real-time drilling data analytics for geohazard detection and wellbore stability: A comprehensive review

Real-time drilling data analytics has emerged as a transformative approach for improving geohazard detection and wellbore stability in modern drilling operations. This paper presents a comprehensive review of the fundamental concepts, technologies, and methodologies underpinning data-driven drilling systems. It examines the principles of geohazards and wellbore stability, followed by an analysis of real-time data acquisition technologies, including logging-while-drilling (LWD) and measurement-while-drilling (MWD), as well as advanced data processing and machine learning techniques. A comparative evaluation of machine learning, physics-based, hybrid, real-time, and pre-drill models is conducted to assess their performance, accuracy, and limitations. The review highlights that machine learning and real-time analytics provide superior adaptability and predictive capability, while physics-based models offer essential interpretability. Hybrid approaches are identified as the most promising solutions for integrating accuracy with reliability. Key challenges such as data quality, model generalization, computational demands, and integration complexity are also discussed.
Furthermore, the paper explores emerging opportunities, including digital twin technology, advanced artificial intelligence methods, and automated decision support systems. These innovations are expected to enhance predictive performance and operational efficiency. The study concludes that the integration of real-time analytics with advanced modeling techniques is essential for achieving safer, more efficient, and cost-effective drilling operations in increasingly complex subsurface environments.