PREDICTION OF PERMEABILITY OF OIL AND GAS LAYERS USING ARTIFICIAL NEURAL NETWORKS

This article focuses on predicting the permeability of oil and gas reservoirs using artificial neural networks (ANN). By utilizing data sets from oil and gas wells, comprehensive preprocessing was conducted, including feature selection, scaling, and normalization to ensure the robustness of the models. The effectiveness of ANN in predicting the permeability of underground formations was evaluated using petrophysical data from wells in the Bukhara-Khiva oil and gas region. A precise permeability prediction model was created using key petrophysical parameters such as gamma rays (GR), resistivity (RT), sonic (DT), density (RHOB), and neutron porosity (NPHI). To enhance model performance, the dataset underwent complete preprocessing, including normalization and feature selection. The model's performance was assessed through MSE, R², and MAE metrics, demonstrating higher accuracy compared to traditional linear regression models. The results indicate that the ANN model provides highly accurate permeability predictions. The findings offer valuable insights for optimizing exploration and production strategies in the oil and gas industry, highlighting the superiority of machine learning and neural network models over traditional methods in subsurface resource evaluation.