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- W2887261694 abstract "Summary Permeability prediction in carbonate reservoir is so difficult because of high heterogeneity causing by complicated diagenetic modification and various depositional facies. In this study, available routine core data from 4 wells of Ilam reservoir in an Iranian oil field have been used for permeability prediction and also Hydraulic Flow Unit (HFU) definition in order to model permeability across the field. Permeability prediction have been carried out using 2 approaches including direct permeability calculation from petrophysical logs as well as core data and HFU as second approach. In first approach, Artificial Neural Network has been selected for supervised permeability prediction using electrofacies and raw data. In second approach, Winland rock typing method has been applied in cored interval and then, FZI estimated for un-cored intervals. The resulted FZI and permeability logs have been modeled via static modeling in Petrel software. Based on FZI ranges, rock types were defined in model the related rock type equation has been used to estimate permeability in each rock type. Finally, two resulted permeability models have been compared together approving the validity of both methods for permeability prediction and distribution in the model. The correlation coefficient between two permeability models is 0.90 with similar distribution trend." @default.
- W2887261694 created "2018-08-22" @default.
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- W2887261694 date "2018-06-11" @default.
- W2887261694 modified "2023-09-23" @default.
- W2887261694 title "Permeability Prediction and Modelling Using Different Approaches in Ilam Carbonate Reservoir in an Iranian Oil Field" @default.
- W2887261694 doi "https://doi.org/10.3997/2214-4609.201800803" @default.
- W2887261694 hasPublicationYear "2018" @default.
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