History matching (HM) is a complex process that aims to increase the reliability of reservoir‐simulation models. HM is an inverse problem with multiple solutions that call for probabilistic approaches. When observed data are integrated with sampling methods, uncertainty can be reduced by updating the uncertainty distribution of the reservoir properties. This work presents a practical methodology to systematically reduce uncertainties in a multiobjective assisted HM while dealing with multiple scenarios and assimilating well and 4D‐seismic (4DS) data quantitatively. The frequency‐distribution update goes through an iterative process. The distribution of the current iteration is combined with the histogram generated using the best‐matched simulation scenarios from the current iteration to generate the updated distribution. To evaluate the matching quality, multiple local objective functions (LOFs) are independently evaluated, enabling the identification of LOFs that expose the need for reparameterization. This quantitative process was applied in two phases: Phase 1, in which only well data were used to constrain the scenarios, and Phase 2, when 4DS data were added. The methodology was successfully validated against a synthetic benchmark case of medium complexity, with the production‐history data generated at a fine scale (reference model). Each iteration increased the number of matched scenarios, demonstrating good convergence. Most of the reservoir properties had uncertainty reduced gradually while avoiding the premature reduction of the uncertainty range (minimizing convergence to an incorrect solution). Local probabilistic perturbations were conducted on the petrophysical realizations in the regions around the wells that manifested LOFs, which hindered the match. The method efficiently achieved multiple matched simulation scenarios, with all (87) LOFs evaluated within the defined tolerance range. The 4DS data were included regionally with an acceptable increase in computation time. In both phases, the matched simulation scenarios presented production forecasts similar to the reference model. The quantitative assimilation of 4DS data generated scenarios that forecast production with less variability than did scenarios generated without 4DS data. This was expected for this study because the 4DS data provided do not present noise or artifacts.