Abstract: Since its discovery in 2010, the NEAG 2 has been one of the most productive oil fields of the Badr El-Din Petroleum Company (BAPETCO) in the northern Western Desert of Egypt. The Albian-Cenomanian reservoir system has a unique performance but suffers from several issues hindering its production. The latest production report in 2023, NEAG-2 Field was producing 1760 bbls of oil with 36500 bbls of water, i.e., 95% water cut. Despite that, the field has reached a 39% recovery factor but the reservoir forecast suggests a much higher recovery factor. Therefore, the NEAG 2 Field requires a comprehensive geological model to depict its reservoir heterogeneities better. We introduce a solid and integrated workflow to investigate the reservoir characters among different scales of geological heterogeneity and offer solutions to overcome some data gaps. After characterizing the reservoir elements by the structural, stratigraphic, petrographic, and petrophysical analyses, a machine learning-based method was applied to overcome the missing whole rock cores in creating a detailed electro-facies log for all field wells. The Neural-Network algorithm required the facies types to be grouped into definitive reservoir qualities to be applied. The resultant electro-facies log had a very good match with the input logs, which validated the facies grouping. This was followed by the porosity-permeability transforms, estimated from mobility data, to create a permeability curve for all field wells, despite the unavailability of core data. The reservoir was categorized into three rock types, each with a specific range of quality, signifying their different flow abilities which were supported by dynamic data. The Lower Bahariya-Kharita in NEAG 2 was ultimately concluded to be a complex heterogeneous reservoir with varying flow abilities and production behaviors. The recovery factor mismatch is due to unrecovered reserves, and a new production strategy should be introduced to reach the ultimate recovery. This integration of geologic and dynamic data is strongly recommended for any reservoir characterization study to avoid oversimplifying the reservoir system and to design the right reservoir development plan.