As an unconventional resource, shale reservoirs recently have attracted considerable attention in the petroleum industry. Shale plays are highly heterogenous vertically and laterally and are characterized by rapid changes in mineral composition. Thus, identification of dominant lithofacies is a key issue in the development of shale oil and gas reservoirs. In this study, various existing lithofacies in a shale section as a target unit in the Qingshankou Formation are divided based on organic matter content, sedimentary structure, and mineral composition. To delineate the electrofacies from the log, the multiresolution graph-based clustering (MRGC) is used to optimize the conventional logs that are sensitive to the electrofacies clustering analyses. Based on the principle of lithofacies identification, the electrofacies are artificially related to the lithofacies as well. This was done by analyzing the petrophysical characteristics of various shale lithofacies, to enable obtaining the main log parameters for the facies of the lacustrine shale section understudy. The results showed that by considering the underlying geologic criterion of each lithofacies, the MRGC method is able to correlate geophysical characteristics of each identified electrofacies for an optimal selection of six lithofacies.