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Abstract

Analysis of heterogeneous gas sand reservoirs is one of the most difficult problems. These reservoirs<br>usually produced from multiple layers with different permeability and complex formation, which is often<br>enhanced by natural fracturing. Therefore, using new well logging techniques like NMR or a<br>combination of NMR and conventional open hole logs, as well as developing new interpretation<br>methodologies are essential for improved reservoir characterization. Nuclear magnetic resonance<br>(NMR) logs differ from conventional neutron, density, sonic and resistivity logs because the NMR<br>measurements provide mainly lithology independent detailed porosity and offer a good evaluation of<br>the hydrocarbon potential. NMR logs can also be used to determine formation permeability and<br>capillary pressure.<br>This paper concentrates on permeability estimation from NMR logging parameters. Three models used<br>to derive permeability from NMR are Kenyon model, Coates-Timer model and Bulk Gas Magnetic<br>Resonance model. These models have their advantages and limitations depending on the nature of<br>reservoir properties. This paper discusses permeability derived from Bulk Gas Magnetic Resonance<br>Model and introduces neural network model to derive formation permeability using data from NMR and<br>other open hole log data. The permeability results of neural network model and other models were<br>validated by core permeability for the studied wells.

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/content/papers/10.3997/2214-4609-pdb.248.004
2010-03-07
2024-03-29
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