Reservoir characterization is one of the key stages in oil and gas exploration, appraisal, development, and optimal production. During the exploration phase, core analysis and well logging are basic information obtained after the pay zone identifcation. This study aims to integrate the concept of electrofacies (EFs) with hydraulic fow units (HFUs) to efectively characterize Permo–Triassic carbonate formations at the Kangan giant gas feld in southwestern Iran. For this purpose, well log data collected from 10 drilled wellbores and 490 core data have been analyzed based on similar clusters and statistical features in which rock-type approaches were used in order to integrate petrophysical data with facies. In particular, we used a multiresolution graph-based clustering (MRGC) method to determine EFs and the probability plot method, histogram analysis, and the plot of reservoir quality index versus the normalized porosity to identify HFUs. Based on the data, fve electrofacies and six HFUs were identifed. To establish a good connection between the electrofacies and the pay zones, we analyzed wells’ cross sections to compare lithology, production potential in each zone, rock type, and amount of shale and eventually to adapt facies to fow units and determine the best quality reservoir zone. The main diference between this work and other studies in the literature is adopting a systematic approach based on integrating the geology (via EFs analysis) and engineering examination (via HFUs) to accurately characterize the hydrocarbon-bearing formations. The results of this study help in rapid and cost-efective carbonate reservoir characterization by combining electrofacies clustering and HFU analysis based on core and log data, which are available and routine information in all oil/gas felds. This, in turn, assists in developing the feld, using more appropriate production and EOR scenarios, and even locating proper perforation sites.

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