Abstract Drilling optimization is an essential component of the oil and gas sector, focused on enhancing efficiency, safety, and cost-effectiveness. Historically, drilling operations have encountered difficulties associated with erratic data interpretation, equipment malfunctions, and inadequate drilling settings. As the sector transitions to digitalization, AI provides disruptive solutions that utilize historical data, real-time monitoring, and predictive analytics to improve drilling performance. Integrating AI into drilling operations helps alleviate prevalent challenges such as elevated non-productive time, intricate geological conditions, and the potential for equipment failures. AI facilitates data-driven decision-making, hence improving operational predictability and minimizing human error, both of which are crucial for contemporary drilling operations. This study examines the fundamentals of drilling optimization, the challenges associated with existing approaches, and the potential of AI-driven solutions to mitigate these challenges. Additionally, it offers a framework for the integration of AI into drilling operations and examines case studies that illustrate successful deployments.
Tayo Ajimoko (Mon,) studied this question.
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