Machine-Learning Approach Predicts Gel Strength of Drilling Fluid While Drilling

Nov 02, 2024

Accurately estimating gel strength is paramount for optimizing drilling operations and preventing cuttings from settling at the wellbore’s bottom. Traditional methods rely on rotational viscometers, which are time-intensive, equipment-dependent, and lack real-time monitoring capabilities. This study underscores the feasibility of leveraging machine learning (ML) as a practical tool for predicting drilling-fluid gel strength, offering real-time monitoring and precise predictions to enhance drilling efficiency, safety, and automation initiatives.

The gel strength of drilling fluid is assessed using a viscometer, focusing on the 3-rev/min reading obtained after agitating the fluid at 600 rev/min to disrupt any gel formation. Initially, the measurement is taken when the mud reaches a static state for 10 seconds.