HYPOTHESIS: The main hypothesis is that the aggregation process for nanoparticles (NPs) propagating in porous media is affected by the structure of the flow field as well as by the properties of the primary NPs. If this were true, then the aggregation could be predicted and controlled. However, to obtain reliable results from computations, one needs to account for the interactions between the NPs as well as the details of the fluid velocity, thus making advances over prior efforts that either ignored the aggregation of NPs, or used probabilistic methods to model aggregation. EXPERIMENTS: Computational experiments were conducted using the lattice Boltzmann method in conjunction with Lagrangian particle tracking (LPT). The LPT accounted for the physicochemical interaction forces among NPs. Computationally obtained aggregation kinetics and fractal dimensions of Cerium oxide (CeO2) particles, suspended in potassium chloride (KCl) solutions with different concentration, were verified against experimental results. The model was then employed to investigate the effects of ionic strength, fluid velocity, and particle size on the aggregation kinetics and the aggregate morphology, as NPs propagated in the pore space between randomly packed spheres. FINDINGS: The aim of this study was to develop a computational model to simulate the aggregation of NPs and obtain the morphology of aggregates in confined geometries, based on the physics of NP interactions and the flow field. The most important factor that impacted both the aggregation process and the aggregate structure was found to be the concentration of the electrolyte. The pore velocity influenced the aggregation kinetics and the NP fractal dimension, especially in diffusion-limited aggregation. The primary particle size affected the diffusion-limited aggregation kinetics and the fractal dimension of reaction-limited aggregates noticeably.