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Abstract Objective/Scope Supervisory Control and Data Acquisition (SCADA) has become a trendsetter in modern oilfield management. Although SCADA systems are revolutionary in their ability to collect real-time operational data, they are generally standalone and not fully integrated with other platforms, such as production history, simulation models, and asset management systems. This creates data silos that hinder the holistic use of data for optimizing production across functions. This paper presents a solution to this challenge: a digital twin dashboard technology connected to real-time operational data for improved decision-making. Methods, Procedures, Process The new concept of developing a digital twin dashboard starts with creating a virtual digital twin object by building a physical model of surface facilities and validating the data. Then, the digital twin framework is connected to real-time operations on a dashboard via a simulator server API. The next stage of development involves building a feature framework that enables four process domains: Prediction, Automation, Digital Twin Optimization, and Surveillance Analytics. This product stores database history, provides interactive data visualization, and offers optimization in one holistic system. Results, Observation, and Conclusions The digital twin dashboard successfully captured the surface facilities optimization process by integrating real-time data. Using operational data simplified the modeling process and reduced delays in production optimization. Performance improvement is achieved by creating four feature domains: Prediction (decline curve analysis, workover and well service scheduler, and flow assurance inhibitor demand forecasting).Automation (well test validation, automatic hydraulic matching for nodal analysis, reservoir pressure history matching, and daily production allocation and reporting).Digital Twin Optimization (surface network history matching, choke optimization, and bottleneck identification)Surveillance Analytics (field and well performance monitoring, surface equipment monitoring, emission monitoring, and system anomaly detection). This integration provides a more holistic view of production optimization decision-making that all functions can utilize. Novel/Additive Information The further development of digital twin technology will address data silos in oilfield optimization by integrating real-time operational data with engineering simulation efficiency. The result will be accurate, holistic optimization decisions that specifically reduce volume downtime and debottleneck surface facilities. This approach can be applied to many oil fields for efficient modeling.