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The use of reactive transport modeling (RTM) is increasing in the oil and gas industry for assessing the geochemical impact (e.g. scaling and souring) of various activities, such as waterflooding for improved oil recovery (IOR) and CO2 storage. RTM is a technique that integrates fluid flow, transport of heat and solutes, and geochemical reactions. It can be used to model fluid compositional changes as well as rock mineralogical changes, caused by geochemical reactions, under flowing conditions. We use our in-house reservoir simulator (MoReS), coupled to geochemical software (PHREEQC), to carry out RTM. Simulations are based on the mixed solvent electrolyte (MSE) model from OLI Studio, a standard tool used by production chemists, enabling accurate computation of aqueous chemical reactions and partitioning of components between solid, fluid and gas phases. Over the last few years we have used RTM to make scale predictions for several waterflooding projects around the globe. In this paper we will show results from these field cases and highlight the most important findings. In brief, these are: Enabling mineral precipitation reactions in flow calculations improves the match between measured and simulated production water (PW) chemistry.Full 3D reservoir models capture different flow paths arriving/mixing near production wells, enabling an improved match between historical and simulated PW chemistry. Simplified (1D/2D) models are sufficient for predicting the magnitude of scale deposition and screening scale prediction uncertainties when little is known about reservoir connectivity (e.g. new developments).Inclusion of clay mineral cation exchange reactions significantly modifies the evolution of the injected water composition during migration through the reservoir. As a result, this impacts the reservoir deposition of scaling minerals (e.g. barite) and the scaling potential of production wells. Characterization of cation exchange properties of clay minerals in reservoirs is therefore recommended. The developed workflow, based on learnings from various projects, is now used to forecast scaling risks in new projects and supports ongoing projects in mitigating risks (e.g. selection/timing scale squeezes).