In this paper, an integrated workflow based on recent geoscience data is presented for assessing the reservoir characterization and structural interpretation of the Burgan formation, a highly productive formation in the Abadan plain, SW Iran. Utilizing newly acquired high-resolution SEM images, we evaluated the pore size, pore distribution, and pore aspect ratio of Burgan formation. The watershed segmentation algorithm is also capable of detecting throats and closed pores. The porosity fractions from SEM images are used for calibration of the porosity log at several well locations in order to perform petrophysical modeling. Since the facies behavior is complex in the study area, we utilized supervised Bayesian classifier using P-wave velocity, density, and facies log dataset. The confusing matrix and machine learning metrics including Accuracy (97.01%), Precision (93.88%), F1 Score (94.16%), and False Positive Rate (2.52%), indicate that the classifier has been properly trained at well locations. A reasonable match is evident between the modeled petrophysical parameters and the true (core) porosity and water saturation at the location of the test well. Furthermore, we have demonstrated the validity of assumptions concerning the dominance of extensional structure in the Abadan plain by using interpreted seismic data. The presented workflow can be used to optimize drilling operations and reduce risks in similar geological settings in the studied formation.