A digital twin modeling and application for gear rack drilling rigs lifting system

Area of Science:

• * Oil and Gas Engineering
• * Digital Transformation
• * Industrial IoT

Background:

• * The oil and gas sector is undergoing significant digital transformation, with digital twins emerging as key enablers for real-time data analysis and predictive insights.
• * Intelligent oil and gas fields offer substantial potential through the effective implementation of digital twin technology.
• * Existing digital twin frameworks lack specificity for the unique demands of gear rack drilling rig systems.

Purpose of the Study:

• * To propose a novel digital twin framework specifically designed for gear rack drilling rigs.
• * To detail the composition, characteristics, and behavioral rules of digital twins within the context of drilling rig lifting systems.
• * To provide a foundational and methodological guide for implementing digital twin technology in the oil and gas industry.

Main Methods:

• * Development of a digital twin framework integrating mechanism modeling, real-time performance response, instantaneous data transmission, and data visualization.
• * Construction of mechanism models to analyze dynamic gear performance and support unit response in lifting systems.
• * Utilization of sensor-based monitoring for real-time data acquisition, combined with machine learning for enhanced dynamic performance prediction.

Main Results:

• * Successful illustration of the framework through case studies on the transmission and support units of a drilling rig lifting system.
• * Enhanced prediction speed and accuracy of dynamic performance achieved through the synergy of mechanism modeling, machine learning, and real-time data analysis.
• * Modular components developed for streamlined creation of high-fidelity digital twins, supporting diverse application scenarios.

Conclusions:

• * The proposed digital twin framework provides a robust methodology for understanding and implementing digital twin technology in oil and gas drilling operations.
• * The integration of mechanism modeling, real-time data, and machine learning significantly improves predictive capabilities for drilling rig systems.
• * This work serves as a foundational guide, paving the way for the advancement and broader adoption of digital twins in the oil and gas industry.