Neural network prediction of porosity and permeability of heterogeneous gas sand reservoirs using NMR and conventional logs

Analysis of heterogeneous 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. Therefore, using new well logging techniques like NMR or a combination of NMR and conventional openhole logs, as well as developing new interpretation methodologies are essential for improved reservoir characterization. Nuclear magnetic resonance (NMR) logs differ from conventional neutron, density, sonic and resistivity logs because the NMR measurements provide mainly lithology independent detailed porosity and offer a good evaluation of the hydrocarbon potential. NMR logs can also be used to determine formation permeability and capillary pressure. In heterogeneous reservoirs classical methods face problems in determining accurately the relevant petrophysical parameters. Applications of artificial intelligence have recently made this challenge a possible practice. This paper presents a successful application of Neural Network (NN) to predict porosity and permeability of gas sand reservoirs using NMR T2 (transverse relaxation time) and conventional open hole logs data. The developed NN models use the NMR T2 pin values, and density and resistivity logs to predict porosity, and permeability for two test wells. The NN trained models displayed good correlation with core porosity and permeability values, and with the NMR derived porosity and permeability in the test wells.