Time-lapse (4D) seismic data sets have proven to be extremely useful for reservoir monitoring. Seismic-derived impedance estimates are commonly used as a 4D attribute to constrain updates to reservoir fluid flow models. However, 4D seismic estimates of P-wave impedance can contain significant errors associated with the effects of seismic noise and the inherent instability of inverse methods. These errors may compromise the geological accuracy of the reservoir model leading to incorrect reservoir model property updates and incorrect reservoir fluid flow predictions. To evaluate such errors and uncertainties we study two time-lapse scenarios based on 1D and 3D reservoir model examples, thereby exploring a number of inverse theory concepts associated with the instability and error of coloured inversion operators and their dependence on seismic noise levels. In the 1D example, we show that inverted band-limited impedance changes have a smaller root-mean-square (RMS) error in comparison to their absolute broadband counterpart for signal-to-noise ratios 10 and 5 while for signal-to-noise ratio (S/N) = 3 both inversion methods present similarly high errors. In the 3D example we use an oilfield benchmark case based on the Namorado Field in Campos Basin, Brazil. We introduce a histogram similarity measure to quantify the impact of seismic noise on maps of 4D seismic amplitude and impedance changes as a function of S/N levels, which indicate that amplitudes are less sensitive to 4D seismic noise than impedances. The RMS errors in the estimates of water saturation changes derived from 4D seismic amplitudes are also smaller than for 4D seismic impedances, over a wide range of typical seismic noise levels. These results quantitatively demonstrate that seismic amplitudes can be more accurate and robust than seismic impedances for quantifying water saturation changes with 4D seismic data, and emphasize that seismic amplitudes may be more reliable to update fluid flow model properties in the presence of realistic 4D seismic noise.