The choice of adequate scaling procedures is a crucial issue to geological reservoir modeling. The integration of fine scale geophysical well logging and petrophysical data with seismic data, and the procedures of transferring information from detailed geological models to large scale, dynamic reservoir models have been theme of intense debate in the literature. In this work, we propose a new approach that is focused on filtering well logging data in order to control data entry for seismic inversion and subsequent reservoir modeling. Spectral analysis using Fast Fourier Transform is performed for a set of wells arranged around a single line along the axis of the reservoir. Lithological geophysical logging (GR, sonic, litho-density and neutron porosity log) were fourier-transformed and their power spectra were analyzed. Observed main frequencies were compared to geological derived information in order to retain only significant architectural elements of the reservoir. Logging data are filtered for these frequencies and the resulting medium scale features are used to model synthetic wells from which the data for seismic inversion were derived. This procedure minimizes higher frequency entries at a level where they can be adequately scrutinized under a very effective geological framework. The proposed approach effectively handles with the various scale levels of geophysical and geological acquisition by filtering information from detailed logging data, retaining important geologic features characterization, and avoiding the introduction of high frequency noises for seismic inversion algorithms.