A Systematic Review of Digital Twin Technology Integration into Well and Reservoir Fluid Management Workflows for Real-Time Subsurface Monitoring

Digital twin technology creates continuously updated virtual replicas of physical petroleum production systems through real-time data assimilation, offering transformative potential for well and reservoir fluid management through dynamic model-based decision support. This narrative review draws on published case literature and methodological evidence across petroleum engineering, digital technology, and organizational management within a five-dimension maturity framework covering data architecture completeness, model updating frequency, production optimization functionality, human-machine interface design, and organizational adoption evidence. IoT monitoring deployments, cloud infrastructure scaling, ensemble data assimilation, machine learning production forecasting, blockchain-enabled audit trails, and compliance governance from adjacent digital sectors provide organizational infrastructure analogues. Niger Delta multi-reservoir well performance evidence provides regional context for evaluating digital twin applicability in complex clastic settings. Key findings confirm that successful implementations combine physics-based reservoir simulation with data-driven components within governance frameworks providing appropriate human-machine decision allocation. A structured methods section, maturity model table, implementation summary table, and four-layer architecture framework diagram are provided.