A Model-Driven Approach for Situational Intelligence & Operational Awareness

In order to design, operate, and maintain an oil and gas facility, one must first understand its behavior. A model-driven engineering and operation solution is required to analyze and identify problems early on and then improve design to ensure further problems are less likely. Predictive models are already shaping our experiences. They recommend products and services based on our habits. Predictive models of electrical power networks serve as a “digital twin” of the system including network topology, engineering parameters, and other pertinent information with real-time data acquired for depicting the actual operation of the system. A clear and thorough understanding of the operational system increases uptime and reduces the number of unnecessary shutdowns. Predictive simulation models help engineers and operators increase their understanding of systems in a cost-effective and repeatable environment by offering Situational Intelligence & Automation. This paper will include the benefits of adding such a system, the challenges that must be overcome and the lessons that have been learned from the implementation of several of these systems. It will also serve as a handbook on justification for a model-driven power management and automation of oil and gas facilities.