Integrated automation and electrical control systems are vital for project optimization, system simplicity and operational flexibility in electro-intensive O&G plants. Value of integrated systems through the life-cycle of the plant from design & engineering to construction and operation is widely understood as having a positive impact on CAPEX and OPEX as well as the reliability of operations. Digital twins of the plant and the process hold key information to maximize operational profitability and optimize asset performance. Next generation of digital-ready integrated process and power management system architectures enables digital performance management of plant-wide assets and processes. An evolving approach is the integration of power and process systems throughout the lifecycle of the plant, commencing at the plant design phase and progressing to the operational stage. The improved operational efficiency brought up by integrated power and process control systems through greater situational awareness, improved reliability, and faster troubleshooting extends the value of this integrated approach to operations & maintenance lifecycle. The comprehensive approach to power and process integration is achieved through seven value driven strategies that spans the lifecycle of a process plant.

With the advent of robust and powerful IIoT Edge Devices, it is now possible to deploy Machine Learning models at the boundaries of a production network, i.e. using Smart Nodes directly at the wellhead that runs analytics in near real-time. These Smart Nodes, when paired with Augmented Intelligence capabilities, allow subject matter experts to interact with Machine Learning models and help improve their accuracy over time. This, in turn, helps increase confidence in data-based results and enables operators to make informed decisions. This paper will define and discuss an end-to-end architecture on how Augmented Intelligence, in tandem with Edge Analytics, can be implemented in the upstream production environment. Results, methodologies and lessons learnt from an Edge Analytics solution deployed on Rod Pump wells will be discussed in this paper.