We modeled surface-to-borehole controlled-source electromagnetics to assess the sensitivity of electromagnetic fields to the detection and evolution of the waterfront position in a Saudi Arabian carbonate reservoir. We studied a test well drilled for research purposes at the edge of the current waterfront. The 3D reservoir saturations were obtained by using a black oil simulator and later converted to resistivity using Archie’s empirical relation. The anisotropic overburden model was derived from the upscaling of triaxial resistivity logs acquired from the surface to reservoir depth in the well. The modeling study was performed by the combination of 3D finite-element time-domain and 3D finite-difference frequency-domain methods using surface galvanic sources in radial and transverse polarization modes with borehole receivers. Results indicated that the electric-field components have the largest sensitivity to static reservoir resistivity distributions and to the time-lapse resistivity changes. The vertical component of the electric field, in particular, showed the largest sensitivity to reservoir changes and obtained the best spatial resolution after inversion. A comparative analysis of the noise floor achievable in a vertical well by existing electromagnetic sensors versus the strength of the field changes over a two-year time-lapse scenario indicated that the electric field is the only electromagnetic component showing a magnitude of the change above the estimated noise floor. The obtained results suggested that the detection and monitoring of waterflooding around producing wells is feasible within the sensitivity offered by current borehole sensing technology using a surface-to-borehole acquisition configuration. In this framework, the combination of multicomponent electric and magnetic sensors deployed in vertical and horizontal wells would provide enhanced resolution for the reservoir properties by enabling a better description of anisotropic effects.