Faculty of Chemical, Petroleum and Gas Engineering, University of Science and Technology, Tehran, I.R. IRAN
Water flooding is one of the common secondary recovery methods. One of the major problems of water flooding is the production of water. Consequently, water breakthrough happens quickly. In these conditions, the production of the reservoir is not economical. In comparison to conventional flooding, cyclic water flooding (CWF) produces more oil and less water. CWF is performed to produce oil from a zone with less permeability that cannot be displaced. Studies have shown that oil production is more likely to occur during the period of injector shut-in. To achieve maximum oil production from oil fields and less produced water, optimization methods should be used in the process of cyclic water injection. By controlling and optimizing the well parameters, such as production and injection rates, as well as the economic evaluation of the process, one can achieve this goal. The purpose of this study is the scheduling and optimization of production using net present value (NPV) for this process. In this research, two-dimensional and three-dimensional models from reliable literature data are simulated. After validation and comparison to the literature, the sensitivity of the model for the duration of injection and shut-in of the injector is performed in CWF. To optimize, we used the genetic algorithm, which is an efficient stochastic approach without the requirement of derivative calculation. The results showed that the CWF in a two-dimensional model in water-wet and oil-wet reservoirs has increased in NPV value of 2.26% and 3.9% respectively. In 3D model, the results showed NPV value in the cyclic flooding method in comparison to conventional flooding in the oil-wet reservoir has increased 3.285% and, in the water-wet reservoirs decreased 3.038%. The result of this study suggests CWF as a suitable secondary recovery approach in comparison to conventional flooding, especially oil-wet reservoirs.