Integrating Real-Time Drilling Fluid Monitoring and Predictive Analytics for Incident Prevention and Environmental Protection in Complex Drilling Operations
Keywords:
Drilling fluid monitoring, predictive analytics, real-time data, AI in drillingAbstract
In complex drilling operations, real-time monitoring of drilling fluid parameters is crucial for enhancing safety, operational efficiency, and environmental protection. This research proposes the development of an AI-powered real-time monitoring system that integrates predictive analytics and machine learning to analyze drilling fluid data continuously. By collaborating with data scientists and engineers, the system is designed to provide predictive insights that preemptively identify potential issues, such as torque build-up, fluid instability, and safety risks, enabling proactive adjustments in drilling procedures. The study examines the role of predictive data analytics in reducing non-productive time (NPT), enhancing operational safety, and mitigating environmental risks. This research contributes to bridging traditional drilling practices with advanced data-driven risk management approaches, fostering a safer and more environmentally sustainable oil and gas industry.
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