Development of an In-Tank Tuning Fork Resonator for Automated Viscosity/Density Measurements of Drilling Fluids

Maintaining the viscosity and density of drilling fluids within their optimal performance margins is of utmost importance in running a safe and efficient operation when drilling oil and gas wells. Safety and efficiency may be adversely affected by current measurement practices of these two key prope...

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Bibliographic Details
Main Authors: Miguel Gonzalez, Tim Thiel, Chinthaka Gooneratne, Robert Adams, Chris Powell, Arturo Magana-Mora, Jothibasu Ramasamy, Max Deffenbaugh
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9319648/
Description
Summary:Maintaining the viscosity and density of drilling fluids within their optimal performance margins is of utmost importance in running a safe and efficient operation when drilling oil and gas wells. Safety and efficiency may be adversely affected by current measurement practices of these two key properties as they still mostly rely on onsite personnel to carry out periodic manual measurements. These laborious measurement protocols limit the quantity and quality of the produced data. Here we describe an automated system for viscosity/density measurements of drilling fluids based on a tuning fork electromechanical resonator. The resonator works as the frequency-defining element of an oscillator circuit, and a digital gain control algorithm is used to control the feedback loop and actuate the device. The self-oscillating circuit allows the measurement to be repeated several times per second, enabling higher measurement precision and real-time monitoring in changing flows. The resonator was integrated into an in-tank system, which performed continuous measurements at the suction tank of the mud circulating system during drilling operations. Measurements in three different wells are presented. The results were in very good agreement with manual measurements reported from standard mud reports and further highlighted opportunities in detecting operational problems from the real-time data streams obtained.
ISSN:2169-3536