Smart Mud Circulation Loop for Real-Time Drilling Fluid Testing and AI-Driven Composition Adjustment

Abstract This paper presents the design and implementation of an AI-enabled smart mud circulation loop developed for laboratory drilling environments. The system is designed to continuously measure key fluid parameters such as density, viscosity, pressure, and temperature, while employing machine learning algorithms to dynamically adjust mud composition. The objective of the work is to enhance operational efficiency, minimize manual interventions, and deepen understanding of drilling-fluid behavior under wellbore-simulated conditions. The experimental loop integrates industry-grade hardware, including a mud tank, centrifugal pump, piping network, and a suite of sensors, with an automated dosing manifold that allows for real-time property adjustments. A supervisory AI controller links sensor data with predictive models to recommend and implement corrective actions. Controlled laboratory trials demonstrated measurable benefits, including a 25% reduction in manual sampling errors, a 30% decrease in adjustment cycle times, and efficiency improvements of up to 25% under simulated drilling conditions. The novelty of this work lies in establishing a scalable, AI-driven testbed that unites real-time monitoring with automated composition control. By embedding automation into mud formulation and performance tracking, the system advances drilling-fluid testing methodologies and lays a foundation for smarter, more sustainable drilling operations.