Abstract
This paper explores the development of an integrated, real-time predictive model for well control that leverages data from drilling fluids to enhance operational safety and efficiency. Traditional well control methods, often reactive in nature, present significant limitations in managing the risks associated with well control issues, such as blowouts and kicks. By utilizing real-time data from drilling fluids—such as pressure, temperature, flow rate, and mud weight—predictive models enable early detection of potential well control problems, allowing for faster decision-making and proactive management of well stability. Key components of the model include data acquisition systems, real-time analytics, and decision-making algorithms that work together to minimize risks and reduce non-productive time (NPT). The paper also highlights the advantages of predictive well control in improving safety, mitigating operational risks, and its potential future applications in broader drilling operations.
Published Version
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