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It is highly essential to characterize the reservoir accurately as possible to implement enhanced oil recovery and development scenarios. Determining complex variations in pore geometry aids in identifying rock types with similar fluid flow properties. Traditional classification of rock types is based on geologic observations and empirical relations between porosity and permeability. However, for a given porosity, permeability can vary by several orders of magnitude. This reveals that porosity only cannot interpret the permeability variation. The proposed approach to define the existence of different rock types in the reservoir is the “hydraulic flow units’ approach.” The approach aims to enhance the reservoir characterization and predict permeability in un-cored wells to reduce the cost of drilling core samples. The approach involves dividing the reservoir into hydraulic flow units (HFUs). It introduces the concept of reservoir quality index and flow zone indicator (FZI) to identify the HFUs. Each unit is imprinted by certain FZI and said to have similar geological and petro-physical properties. Validation of results was done by integrating the HFUs with core description results—particularly samples’ grain sizes—to ascertain the accuracy of the approach. FZI was then integrated with well logs using multiple regression analysis in order to train the logs to recognize the hydraulic flow units in case of absence of core. A regression model was developed for each flow unit, from which FZI can be estimated from logs only. FZI was then correlated with permeability to compute permeability. Results showed that four HFUs exist in the reservoir. Four categories of grain sizes were identified from core analysis. This emphasized the accuracy of the proposed technique. Besides, integration between core data and well-logging ones showed high degree of correlation between well logs and FZI. Expanding this correlation aids in predicting permeability in un-cored well.

Application of hydraulic flow units’ approach for improving reservoir characterization and predicting permeability