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Chemical flooding is a process that improves recovery efficiency based on injection of a chemical system that contains surfactants and polymers. Surfactants are substances that alter the interfacial tension and promote a miscible displacement with residual oil. Polymers decrease the water mobility and, in this process, it is injected after the surfactant injection to promote an immiscible displacement of the micellar bank. Diverse operational and reservoir parameters can influence the micellar injection process. This work analyses the oil recovery efficiency of this process in reservoirs with characteristics of existing reservoirs in the northeast region of Brazil, identifying the optimal applicability parameters and the possible technical viability with these characteristics. The numerical study was done using the commercial software "Stars -CMG" (Computer Modelling Group). The reservoir parameters evaluated were: porosity, permeability, oil viscosity and initial oil saturation, according to some Brazilian northeast reservoirs characteristics, in order to obtain the applicability of the process with these reservoir characteristics, and the optimal application conditions. Then, an analysis of the operational parameters was carried through, with the objective of optimizing the process. The results showed improved recovery efficiencies for diverse reservoir characteristic encountered in the State of "Rio Grande do Norte". For the cases and reservoir conditions studied, the most critical factor that affects the micellar process was initial oil saturation, and the process optimization showed an increase on cumulative oil and on recovery factor. An increase in oil recovery with small costs is the search of petroleum industries together with academic institutions because of uncertain oil price. Therefore, it may be necessary the study of different processes to improve recovery efficiency such as chemical flooding. Introduction Micellar/polymeric solution injection process in oil reservoir is one of chemical methods used at the moment. The method consists on promoting the displacement of the residual oil trapped in the reservoir, through the injection of an aqueous solution with surfactant and polymer. This technology is based on the concept of interfacial tension between two fluids, where the tensoactive agent promote the decrease of this tension among the fluids that are inside the reservoir, favoring the displacement of residual oil with the injected of solution. The procedure consists of injecting the solution with the purpose of forming a fluid bank that displaces the oil with low interfacial tension, see Figure 1. The surfactant is the responsible one for the decrease of the interfacial tension, since it is a substance that adsorbs at the interface between the two liquids. The polymer role in the solution is to promote the decrease of water mobility, since small concentrations of this substance cause a great increase of the water viscosity. The mobility reduction reduces the formation of fingering of the injected solution through the oleic phase ahead.

The optimization of multi-objective problems from the Pareto dominance viewpoint can lead to huge sets of incomparable solutions. Many heuristic techniques proposed to these problems have to deal with approximation sets that can be limited or not. Usually, a new solution generated by a heuristic is compared with other archived non-dominated solutions generated previously. Many techniques deal with limited size archives, since comparisons within unlimited archives may require significant computational effort. To maintain limited archives, solutions need to be discarded. Several techniques were proposed to deal with the problem of deciding which solutions remain in the archive and which are discarded. Previous investigations showed that those techniques might not prevent deterioration of the archives. In this study, we propose to store discarded solutions in a secondary archive and, periodically, recycle them, bringing them back to the optimization process. Three recycling techniques were investigated for three known methods. The datasets for the experiments consisted of 91 instances of discrete and continuous problems with 2, 3 and 4 objectives. The results showed that the recycling method can benefit the tested optimizers on many problem classes.

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