The main objective of this study is to enhance a Predicted Permeability (K_Pred) by integrating Permeability resulted from the interpretation of more than 100 P.T.A. Initially the predicted permeability was generated using neural network method (Combining core and log data) over all 254 wells penetrating the reservoir.

To achieve this task a number of workflows have been discussed and tested and finally two methods were implemented which resulted in two permeability models.

The first model, consist of generating enhanced permeability maps for each porous zone using Permeability Predicted (K_pred), core and well test data. These maps were used as multiplier in Upscaled model to generate the total permeability then exported to reservoir engineer for simulation.

The second model, consist of generating the enhanced permeability by integrating Permeability Predicted (K_pred), core and well test (KH) under each well (Log scale) in order to capture the dynamic changes of the property. This enhanced permeability was populated in geological model using stochastic methods conditioned to Rock Type and porosity.

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