Automated system for continuous monitoring of drilling fluid parameters

2026

Closed access Metadata (CSL-JSON)
DOI 10.37878/2708-0080/2026-3.04
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

The aim of this study was to develop a hardware–software system for real-time automated monitoring of drilling fluid density, Marsh funnel viscosity, plastic viscosity, and yield point. An automated density measurement device was developed based on variations in the inductance of an electromagnetic system caused by the interaction between a magnetic core and fluids of different densities. Continuous data analysis methods and artificial intelligence algorithms were employed to evaluate rheological parameters. Laboratory and comparative tests were conducted using a standard Fann 35 viscometer at a temperature of 80°C. The developed system provided continuous measurement of drilling fluid parameters with an error not exceeding 2.5%, in compliance with API RP 13B-1 requirements. Comparison with the Fann 35 viscometer demonstrated deviations within the range of 3–5%. The system successfully identified abnormal operating conditions, including increases in viscosity and reductions in yield point, with a response time of less than 30 seconds. The obtained results confirmed the effectiveness of intelligent methods for predicting changes in the rheological properties of drilling fluids. The developed system improves the efficiency of drilling fluid parameter monitoring, minimizes the influence of the human factor, and enables integration into digital drilling management systems. The proposed technology also demonstrates significant potential for predicting drilling complications and expanding system functionality through the inclusion of additional monitoring parameters, such as pH and gas content.