Determination and Interpretation of Electrofacies using SOM Neural Network and its Application to Prediction of Khami Group Facies in Marun Oil Field (South West Iran)

In this research electrofacies and reservoir zonation in 5 wells together with flow unit characterization in the Khami reservoir are investigated in Marun oilfield for the first time. The flow units data are compared with petrographic studies in this oilfield. Based on well logging data as well as clustering method, 5 electrofacies determined and separated. The studied electrofacies are correlated with flow units derived from core porosity and permeability. Capillary pressure tests indicated an increase of amount and porous size from flow unit 1 to flow unit 4 together with increment in their relationship. Based on electrofacies and flow units data along with petrography studies the comparison in reservoir indicated a suitable relationship between electrofacies and lithofacies. Similarity between electrofacies and lithofacies data indicated that the Khami reservoir is almost a petrophysical reservoir reflecting compatible characteristics between productive zones and petrophysical variations. Integration between determined electrofacies and the studied lithofacies presented as a final model in base well and developed all over the Marun oilfield. This model can determine and differentiate areas involving good and bad reservoir quality. The proposed model can apply for developing a static model in the Maron oilfield.