The value proposition of petroleum workflow automation traditionally includes the ability to provide comprehensive production surveillance to streamline efficiencies and avoid unwanted downtime. But this is just the first step in a production workflow automation strategy. The ability to analyze flow measurements and other operational data in real time to continually diagnose and optimize well performance is a major step forward. In its current setup, the proposed method can be applied to continuous gas-lift and ESP-operated oil wells. A central problem when operating gas-lift and ESP wells is to identify and address issues causing underperformance at the correct time. The purpose of this paper is to describe a method for real-time analysis of the performance of artificially lifted wells and then accelerate and improve the diagnosis and decision-making processes. The solution presented here can operate in complex, high-frequency data environments and has the ability to federate multiple data sources. The method is based on proven steady-state nodal analysis techniques and models multiphase flow from the reservoir to the wellhead. The well model is updated, and the model results are displayed as soon as new data arrive, thereby providing the users with the most current information from their well. Available data include pressure gradient curves, inflow and outflow curves, gas-lift or pump performance curves, the current model operating point, and the recommended opportunity point. The real-time measurements combined with the real-time model calculations provide an advanced tool for artificial lift system analysis and troubleshooting. This method is designed to generate actions, such as decreasing or increasing the gas injection rate, pump frequency, and choke setting. The ultimate benefit is the ability to identify performance issues and opportunities at the correct moment in time.

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