Petrophysical Evaluation by Integrating AI and HFU Methods. A Case Study of the Mishrif Reservoir in Southern Iraq

Reservoir quality assessment is important for detecting hydrocarbon-bearing zones and guiding future enhancement strategies. This study presents a detailed petrophysical evaluation of the Mishrif Formation in the Buzurgan Oilfield, which was selected due to its strategic value through its significant remaining reserves which making it an ideal candidate for advanced evaluation techniques. This study aims for shale content, porosity, permeability, water saturation, net to gross, and lithology determination. Well log and core data were used together to establish accurate property estimations. Permeability prediction through conventional methods, like core permeability-porosity correlations, was highly dispersive due to the heterogeneity of the carbonate formation. To ensure accurate permeability prediction, the Hydraulic Flow Unit method was employed with the Bootstrap Forest-AI model. The research results reveal that MB21 is the principal pay zone, which exhibits high porosity, low water saturation (high hydrocarbon saturation), and low shale content. These zone favorable properties make it encouraging for future development through drilling more production wells in this zone. This study presents a novel hybrid approach that integrates classical petrophysical approaches with an AI model, providing a robust platform for reservoir characterization.