Application of Probabilistic Neural Network Technique in Identifying Low Porosity and Low Permeability Gas-Layers in Logging Data

Chong Zhang; Guang Chao Pan

doi:10.4028/www.scientific.net/AMM.58-60.1712

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60Application of Probabilistic Neural Network...

Application of Probabilistic Neural Network Technique in Identifying Low Porosity and Low Permeability Gas-Layers in Logging Data

Abstract:

In view of poor Physical Properties,complex Pore Structure and high saturation of low porosity and low permeability gas layers ,in order to overcome the difficult of fluid property identification in low porosity and low permeability gas layers using conventional method, probabilistic neural network technique was Proposed.According to an example for low porosity and low permeability gas reservoil in Southwest China, Combined with well testing data,logging Response Characteristics of various fluid property layers were analyzed.According the correlation between logging Response Characteristic values and fluid property, PNN was trained and PNN prediction model was established. fluid property in the region layers were identified.The results showed that the PNN prediction model was very promising influid property identification.

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Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

1712-1715

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.1712

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Online since:

June 2011

Authors:

Chong Zhang, Guang Chao Pan

Keywords:

Gas Reservoir, Logging Identification, Low Porosity, Low-Permeability, Probabilistic Neural Network (PNN)

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