Archives of Current Research International

Digital twin technology has moved from a conceptual novelty to an operational necessity across upstream and midstream petroleum engineering. By fusing real-time sensor streams with physics-based and data-driven models, digital twins allow operators to monitor, predict, and optimise drilling, production, and facility performance with a fidelity that static simulation cannot match. This review synthesises the peer-reviewed literature on digital twin applications for real-time oilfield optimisation, examining conceptual foundations, architectural patterns, data acquisition strategies, virtual flow metering, predictive maintenance, production and reservoir optimisation, and the cybersecurity implications of increasingly interconnected assets. The review adopts a narrative rather than systematic methodology, given the breadth and technical heterogeneity of the subject matter, while retaining a transparent and reproducible search protocol. Evidence indicates that digital twins deliver measurable gains in production efficiency, unplanned downtime reduction, and multiphase flow measurement accuracy, although adoption remains uneven and constrained by data quality, interoperability, and validation challenges. Cybersecurity emerges as an increasingly central concern as digital twins tighten the coupling between information technology and operational technology domains. The review concludes that digital twin maturity in the oilfield sector is progressing unevenly across the asset lifecycle, with virtual flow metering and predictive maintenance the most operationally mature applications, while closed-loop autonomous optimisation remains comparatively nascent. Future research priorities include standardisation of digital twin architectures, robust uncertainty quantification, and integrated cybersecurity-by-design frameworks.