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International Journal of Petroleum and Geoscience Engineering
IJPGE 2023, 15 pages - Article ID: IJPGE-2212052112830
Artificial Neural Network (ANN) Prediction of Porosity and Permeability of Shaly Gas Sandstone Reservoir using NMR Data
a Petroleum Engineering Department, College of Engineering, The American University of Kurdistan, Kurdistan, Iraq
b Shell Egypt, Egypt
ABSTRACT
Petrophysical evaluation of shaly gas sand reservoirs is one of the most difficult problems. These reservoirs usually produce from multiple layers with different permeability and complex formation, which is often enhanced by natural fracturing. In this study, we propose a new model to predict porosity and permeability using derived data from NMR. The developed Artificial Neural Network (ANN) model uses the NMR T2 pin values, density and resistivity logs to predict porosity, and permeability for two test wells. The NN trained model has displayed good correlation with core porosity and permeability values, and with the NMR derived porosity and permeability in the test wells. This work focuses on determination of porosity (φDMR) from combination of density porosity, NMR porosity and permeability from NMR logs using Bulk Gas Magnetic Resonance Permeability (KBGMR). Neural network (ANN) technique is used to predict formation porosity and permeability using NMR and conventional logging data. Predicted porosity and permeability have shown a good correlation about 0.912 with core porosity and about 0.891 for permeability in the studied shaly gas sand reservoir.