This paper evaluates nanoparticle preflush to prevent the decline of well injectivity due to fines migration near the wellbore in oil & gas production, or wastes disposal fields. Nanoparticles pre-coated on the rock grains in 1-D porous medium is one of common approaches to prevent the migration of fine particles in reservoirs. In this paper, the positive contributions of nanoparticles to reduce fines migration are hypothesized as the increase of the maximum particles retention capacity of the host rocks due to reduction of repulsive force between nanoparticles-coated rock grains and fine particulates. By applying method of characteristics, series of analytic particles suspension flow solutions considering fine particles attachment/ detachment and particles straining effects are developed for the cases of nanoparticles preflush and two different reference scenarios attributed to content of clay fines in host reservoirs. In comparison of experimental effluent particles concentration history and permeability reduction results, the accuracy of analytical solutions and the proposed theory of nanoparticles affecting fines migration are validated. In addition, the suspended, attached, and strained particle concentration profiles along the 1-D model are compared to indicate the different transport phenomenon of fine particles with or without nanoparticles preflush. Permeability impairment and an associated increase of injection pressure drop (assuming a constant injection rate) are characterized using injection index. The performance of nanoparticles to reduce fines migration and improve well injectivity are evaluated using mitigation index. The analytical solutions explain well how effectively nanoparticles can prevent the decline of permeability and well injectivity through the fixation of mobile fine particles. In practice, the series of analytical solution and hypothesized theory in this paper provide simple and quantitative approach to evaluate and optimize performance of nanoparticles pre-flush to sustain well injectivity for diverse applications. (C) 2017 Elsevier Ltd. All rights reserved.