One of the main challenges in the oil industry is optimizing production while reducing the development cost and maximizing reserves. Real-time optimization has been gaining ground to effectively face the challenge, and it has been applied in the Smart field or Digital field technology. Literature references two optimization loops, the reservoir-centric slow loop and the well-centric fast loop. Intelligent wells are focused on the fast loop and combine wellcentric technologies with monitoring, data analysis, modeling, interpretation and automation to accelerate production, maximize recovery and reduce operating and capital expenditure cost. Benefits of intelligent wells include increased ultimate recovery, accelerated production and cost reduction. Intelligent well equipment can include intelligent completion systems, artificial lift systems, and automated chemical injection systems. Information regarding the optimal intelligent equipment configuration for a well at various points in its life is presented in the sales proposal. However, current practices and processes fail to incorporate or access that information during actual well operations. As a result, the well may not optimize production or add the value that was expected. Closing the well-centric production optimization loop depends on improving the ability to use the data that is recorded and transmitted by downhole and surface sensors. Currently the loop is closed by human intervention which takes a lot of time and effort. An intelligent optimization system that goes beyond human activity to close the loop is presented in this paper. The system can change the traditional monitoring activity from polling to interruptdriven, and can also advise engineers when wells deviate from their expected behavior in ways that prompt diagnosis of emerging, unexpected problems early enough to avoid them. The application of an intelligent production optimization system can result in an improved production with less resources since it removes the need for constant engineer observation and interaction. The paper also provides a vision for well-centric production optimization of automated systems

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