Multi-scale datasets integration for thinly bedded reservoir characterization: an example from the Abu Roash reservoir, East Beni Suef Basin, Egypt
Mohamed I. Abdel‐Fattah, Mohamed A. Aboulmagd, Mohammad Abdelfattah Sarhan, Hamdan Hamdan, Amer A. Shehata
Abstract
Thinly bedded reservoirs are hard to characterize due to heterogeneity and subtle lithological changes that hinder the identification of productive zones. To overcome these challenges, this study integrates seismic data, well logs, core samples and sedimentology to achieve precise reservoir characterization. Structural interpretation revealed a complex graben system bounded by major listric normal faults trending predominantly NW–SE and WNW–ESE. These faults dip to the NE and SW, creating a series of horsts and graben that control sediment deposition and compartmentalizing the reservoir. The analysis identified four distinct lithofacies associations within the Abu Roash ‘G’ (ARG) reservoir, which are key to understanding the reservoir's heterogeneity and potential for hydrocarbon accumulation: bioturbated mudstones, lenticular-bedded siltstone, flaser-bedded sandstone and massive sandstone. The sandstones, deposited in tidal mixed flat or channel environments, represent the primary hydrocarbon-bearing units, while the siltstone and mudstone facies reflect a tidal-dominated shoreline environment. Petrophysical evaluation identified key reservoir zones in oilfields such as Gharibon and Sohba, with oil-bearing intervals ranging from 2 to 9 ft. Despite their thin nature, these zones exhibit excellent reservoir properties, including low shale volume (<15%), high porosity (15–22%) and high hydrocarbon saturation (40–70%). The reservoir is classified into four types (1–4). Types 1 and 2, which comprise massive faintly laminated and flaser-bedded sandstones, demonstrate a superior reservoir quality, with average permeability values of 7.1 and 3.5 mD, porosity of 20.25 and 16%, and oil saturation of 15.8 and 15.3%, respectively. Conversely, Type 3 (siltstones) is considered to consist of fair-quality reservoirs, while Type 4 (mudstones) consists of non-reservoir facies. Data integration boosts recovery development in thinly bedded formations like ARG, with global relevance.