The area of smart well technology, or closed-loop reservoir management, aims at enhancing oil recovery through a combination of monitoring and control. Monitoring is performed with a wide range of sensors deployed downhole or at the surface. These sensors allow for capturing changes in the reservoir conditions, mainly the fluid movement, at different resolutions. Downhole sensors give information of the fluid entering the well and sample only the region immediately adjacent to the well. Reservoir-imaging techniques are based on downhole or surface sensors and image large reservoir volumes typically with a resolution at the ten meter scale. Control is performed by installation of downhole flow control devices that can regulate the fluid inflow from the reservoir into the well ranging from on/off to a large number of settings. Combining monitoring and inflow technology allows using control strategies that mitigate undesired events such as premature water or gas breakthrough. Premature breakthrough of undesired fluids can reduce drastically the oil production and may cause the production well to be shut down. Generally the near-well region in the order of ten meters is poorly imaged. However, in specific reservoir environments the monitoring of the near-well region is strongly required. For example, thin oil rim reservoirs usually have a thickness in the order of few tens of meters and are characterized by early water breakthrough in individual segments of the well. Steam Assisted Gravity Drainage (SAGD) is an enhanced oil recovery technique used in heavy oil reservoirs, where oil is extremely viscous and steam injection is used to facilitate the oil flow. A pair of horizontal wells is drilled into the reservoir only a few meters apart to allow for steam injection and oil production; however, the steam chamber growth and the oil flow are largely unknown. In both these examples a better understanding of the oil displacement process in the first ten meters from the production well could help preventing early breakthrough of unwanted fluids and allow for an implementation of more effective control strategies. We have investigated radar technology as a potential tool able to cover the monitoring requirements needed in specific oilfield environments. This feasibility study was carried out through numerical modeling and laboratory experiments. Through the numerical simulations we conclude that a borehole radar system can be used as a monitoring tool to probe the near-well region of several meters. The main constraint is the formation water electrical conductivity; high conductivity makes attenuation and phase distortion too high for wave propagation. Water/steam front reflections are detectable in low conductivity reservoirs (? < 0.02 S/m). A system performance above 80 dB is necessary to detect reflections in the range of 10 m (chapters 2-3). Additional reservoir constraints are given by a high degree of time-lapse heterogeneity changes of the EM properties and the length of the transition zone from oil to water bearing rocks. The effects of changes in the reservoir can be solved by increasing the data acquisition frequency relative to the rate of the local temporal changes. A gradual transition zone reduces the water reflections, which are not detectable when the transition is in the order of the dominant wavelength of the EM signal (chapters 2-3). Numerical simulations were performed for both simple and complex geological scenarios. A sophisticated analysis was performed coupling electromagnetic and reservoir simulations. This allowed to evaluate the GPR performance in a realistic reservoir environment. Plotting the amplitude of the two-way-time reflected signal as the water advances toward the production well, where the radar system was located, appeared in clear up-dipping events (chapter 3). The metal components of the wellbore casing can destructively interfere with the signal emitted by the radar sensor; however a high dielectric medium around the sensor can increase the amplitude of the reflected signal and overcome the interference problem (chapter 2). Through the laboratory experiments we conclude general considerations on the GPR ability in monitoring oil displacement process governed by water. Water was injected in a meter-scale sand box and all the water flooding experiments presented similar characteristics. As for the modeling results, the amplitude of the two-way-time reflected signal as a function of the experiment time resulted in up-dipping events ascribable to the water front advance. According to the initial water saturation and porosity distribution continuous down-dipping events were associated to the up-dipping ones, forming wedgeshaped reflection features. The monitoring of the flow reflection features could be supported by attribute analysis, in particular, instantaneous frequency demonstrated to be a powerful tool to enhance wedge-shaped events. The analysis of the GPR data agreed with impedance measurements taken simultaneously during the water flooding experiments. The main limitation to the GPR monitoring potential is the electrical conductivity of the residual water. The experiments at a high salinity water injection showed a strong attenuation of the signal and a reduction of the resolution (chapter 4). Through an analysis of measured and modeled GPR signal it was possible to take in consideration the effect of uncertainties on subsurface characterization through full-waveform inversion. Subsurface characterization through full-waveform inversion relies heavily on the accuracy with which the forward model represents the actual GPR-subsurface system. Model errors can propagate through the inversion procedure resulting in wrong parameter estimates. The relative errors in the measured Green’s function are mainly determined by the antenna transfer functions uncertainties. Averaging over a large number of transfer function sets leads to a high-accuracy Green’s function estimate from the data, which leads to small errors in the estimated parameters obtained from full-waveform inversion. Provided the measurement conditions are respected, the inversion experiment adequately reproduces the estimated parameters. As soon as the measurement conditions are not completely respected, e.g., presence of extraneous objects, inversion experiments indicated that the accuracy of the estimates improves when calibration measurements to determine the transfer functions are acquired as close as possible to the measurement location (chapter 5).