IFIOK EUNICE JOHNNY

This research presents a comprehensive systematic review of artificial intelligence (AI) techniques and algorithms employed in predictive analytics for drilling hazard management, specifically focusing on stuck pipe incidents, lost circulation events, and wellbore instability. Drilling hazards collectively account for 30-40% of non productive time (NPT) in global drilling operations, costing the oil and gas industry approximately $8-12 billion annually. Traditional monitoring systems rely on reactive, empirical approaches that fail to provide early warnings, while modern drilling operations generate 1-2 terabytes of data per well, creating opportunities for AI-based predictive solutions. Through systematic analysis of 78 peer-viewed research papers published between 2010-2024, this study evaluates the performance characteristics, implementation challenges, and economic viability of various AI algorithms including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, ensemble methods, and deep learning approaches. The research reveals a clear performance hierarchy among AI methods, with deep learning achieving the highest accuracy rates (90-97%) but requiring substantial computational resources and datasets exceeding 50,000 examples. Traditional neural networks demonstrate optimal balance between performance (88-94% accuracy) and practicality, making them the most widely adopted approach in commercial implementations.