Improving permeability modeling using artificial neural networks in Lower Safa sand reservoir, JG field, Abu El-Gharadig basin, Western Desert, Egypt

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Predicting permeability along and between wells to obtain the 3D spatial distribution is challenging in reservoir modeling. Permeability measurements using core sampling are time-consuming and accord too limited subsurface coverage. This study presents a systematic approach based on artificial neural networks (ANNs) to integrate core data and well logs to overcome these challenges and construct a robust 3D permeability model for the Lower Safa sand reservoir, JG field, Abu El-Gharadig basin, Egypt. The routine core analysis was performed to determine the reservoir quality and heterogeneity. Core-log depth match was performed, and the influential well logs, including effective porosity, bulk density, and deep resistivity logs, were selected to build the permeability model. ANN is applied to integrate the core permeability with the logs to predict permeability logs in the two studied wells. The dataset is randomly split into training, validation, and testing sets to evaluate the model's performance, and different hyperparameters are tuned to improve the prediction accuracy. A detailed comparison between the conventional method and the proposed ANN approach is provided in this study. Results demonstrate that the proposed ANN model was able to improve the permeability prediction performance by capturing the complex relationships between permeability and well logging data. The model achieves outstanding performance, where the coefficient of determination (R2) between the predicted permeability and core permeability is 0.90, 0.91, and 0.88 for the training, validation, and testing datasets, respectively. The developed model was used to predict the permeability logs along the reservoir intervals of the two studied wells. Ultimately, the Sequential Gaussian Simulation algorithm was used to populate the predicted logs in 3D. The outcomes of the study will aid users of deep learning to make informed choices on the appropriate ANN models to use in clastic reservoir characterization for more accurate permeability prediction with limited available data.