Artificial Neural Network Permeability Estimation from NMR Logs in Heterogeneous Tight Gas Sand Reservoir

Gharib M. Hamada and Mousatafa M. Elshafei
Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

Analysis of heterogeneous tight gas sand reservoirs is one of the most difficult problems. Many tight formations are extremely complex, producing from multiple layers with different permeability that is often enhanced by natural fracturing. Therefore, looking for using new well logging techniques like NMR in individual bases or in combination with conventional open hole logs and building new interpretation methodology is essential to well define and obtain the representative reservoir characterizations. 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 a good evaluation hydrocarbon potential. NMR logs can be used to determine formation permeability and capillary pressure.

This paper concentrates on permeability estimation from NMR logging parameters. There are three models to derive permeability from NMR; Kenyon model, Coates-Timer model and Bulk Gas Magnetic Resonance model. These models have their advantages and limitations related mainly to the nature of reservoir properties. This paper discusses permeability derived from these three models and introduces the artificial network model to derive formation permeability using data from NMR and other open hole logs data. The permeability results of artificial neural model and other models will be validated by core permeability for studied wells.

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