Implementation of an Online Surface Network Model (SNM) for Real-Time Production Optimization

The trial involved modeling a representative section of the surface network—including wells, flowlines, manifolds, etc.—using a hydraulic simulation modelling tool integrated with real time data and updated digital twins of associated wells and PVT's. Calibration of the model was carried out using historical and real-time data to ensure accuracy in pressure and flow predictions. A collaborative approach was adopted, involving production operations, subsurface teams, and digital specialists. The project also focused on aligning the SNM output with field measurements and identifying potential operational scenarios that could be simulated to support decision-making.

The implementation of the Online Surface Network Model (SNM) demonstrated significant value in enhancing both the speed and quality of operational decision-making. Traditionally, engineering teams relied on offline models, which often required several days to compile data, perform simulations, and communicate results. With the SNM integrated directly into the live data environment, the team was able to perform simulations and analyze network scenarios in near real-time, greatly improving responsiveness and reducing turnaround time from days to minutes. A major advantage observed during the project was the model's ability to visualize and quantify well-level and node-level performance, allowing for granular tracking of production inefficiencies and bottlenecks. Using this insight, the team was able to identify and prioritize a set of optimization actions with the potential to deliver oil gains equivalent to approximately 2% of total station production—a considerable opportunity, especially within a brownfield context. Beyond production improvements, the model also enhanced well-test planning, supported proactive maintenance scheduling, and improved coordination between field operations, production engineering, and planning teams. These results reinforced the SNM's role not just as a simulation tool, but as a practical, decision-support solution with the potential to scale across larger network areas and contribute to long-term value realization.

This project represents an early-stage application of a live-integrated SNM within a brownfield environment. Unlike traditional static models, it provided real-time operational insights and enabled virtual metering for non-instrumented sections. The project also explored integration with production allocation systems, offering a practical pathway for future scalability. The experience highlights both the technical and organizational considerations essential for adopting digital twin technologies in legacy surface networks.