Neural Network Prediction of Porosity and Permeability of Shaly Gas Sandstone Reservoir Using NMR Data

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 Neural Network (NN) model uses the NMR T2 pin values, and 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 (DMR) from combination of density porosity and NMR porosity and permeability from NMR logs using Bulk Gas Magnetic Resonance Permeability (KBGMR). Neural network (NN) technique is used to predict formation porosity and permeability using NMR and conventional logging data. Predicted porosity and permeability have shown a good correlation with core porosity and permeability in the studied shaly gas sand reservoir.