The Application of Digital Twin Technology Integrated with AI in Intelligent Oilfield Production

Abstract

Based on actual engineering practices in oilfields, this paper systematically presents the architecture and operational mechanisms of integrating digital twin and AI technologies. Focusing on three core application scenarios—optimization of oil production schemes, predictive maintenance of oilfield equipment, and intelligent energy scheduling across the entire field—the paper deeply analyzes technical implementation pathways, methods, practical outcomes, and application advantages through real-world projects at Changqing, Shengli, and Daqing oilfields. Practical experience demonstrates that the integration of digital twin and AI technologies effectively addresses longstanding challenges in traditional oilfield operations, significantly enhancing precision management, reducing operational costs, minimizing equipment failure downtime, and optimizing energy consumption structures. This technology provides solid technical support for the digital transformation and intelligent upgrading of oilfields, improving quality and efficiency while enabling green and low-carbon production. It exhibits strong engineering practicality, implementability, and broad industry promotion value, offering reliable reference for intelligent retrofitting projects in similar oil and gas fields.