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Chemical Enhanced Oil Recovery (EOR) processes comprise a number of flooding techniques aimed at increasing the operational life of mature oilfields. Among these, polymer flooding is one of the most developed; its functionality is to increment the aqueous viscosity, avoiding the formation of viscous fingering. Reservoir simulators consider this influence as well as other physical properties (e.g., adsorption, permeability reduction). However, the polymer degradation is usually not considered even though it plays a critical role in the viscosity. In this paper this mechanism is analyzed and coupled with the previously mentioned physical phenomena in order to present a complete study of their influence in the EOR process. Moreover, since a fully second-order accuracy scheme is used along with a Total Variation Diminishing (TVD) flux-limiting function, the influence of the latter on the recovery factor is also discussed. Results showed that the negative effect of the polymer adsorption was the most relevant physical phenomenon in terms of the oil recovery. Furthermore, the analysis of the discretization of the differential equations showed that traditional, linear first-order schemes created numerical diffusion affecting negatively the macroscopic sweeping efficiency, which disappeared when TVD techniques were used. Reservoir simulators allow determining the desired designing properties for future polymers in relationship with the characteristics of the oilfield to be exploited.

This paper deals with the modelling of genetic mutations, which occur in almost all cells of a living system. The mutated cells display different stages of cancer progression and are contrasted by the action of the immune system cells. This investigation can be of interest in the evolutionary dynamics of cellular systems since the selective pressure on the mutated cells exerted by the immune system is analyzed. The proposed mathematical model is developed by means of the tools of the kinetic theory of active particles. Numerical simulations, obtained considering different values of the parameters in the model, show different emerging behaviors that are typical of the cancer-immune system competition.