For reducing the gap between demand and sources of hydrocarbon-based energy, an effective solution is increasing the oil recovery factor in existing reservoirs. The average recovery factor may disappointingly come down to about 15% in complex reservoirs (Sarma, 2006; Golder Associates). However; by using secondary production approaches such as waterflooding- in which water is injected into the reservoir for conducting the oil toward production wells for more efficiency- up to 70% of the hydrocarbon can be recovered theoretically (van den Hof et al., 2009). So, different aspects of waterflooding modeling, control and optimization studies, have recently attracted much attention by the researchers (Sarma et al., 2006; Shirangi, and Durlofsky, 2015; Grema, and Cao, 2016; Sorek et al., 2017). Although hydrocarbon production is a complex large-scale dynamical process, the operators in the fields mostly manage it just based on their own experiences. Fortunately, widespread applications of advanced instrument and control devices have increased the opportunity to optimize the oil production using model-based control and optimization techniques (Jansen et al., 2008). Nowadays, intelligent reservoirs are generally equipped with appropriate sensors and actuators to monitor the wells and reservoir conditions as well as to control the fluids flow of the producing and injecting wells. It has been perceived that applying advanced monitoring and control systems in reservoirs can significantly increase the hydrocarbon recovery (Glandt, 2005).