Lithologic Reservoirs ›› 2010, Vol. 22 ›› Issue (3): 118-120.doi: 10.3969/j.issn.1673-8926.2010.03.023

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Application of multi-attribute and neural network method to hydrocarbon reservoir prediction

HOU Bin， GUIZhixian， XU Huiqun， HEJiacheng   

  1. Key Laboratory of Exploration Technologies for Oil and Gas Resources， Ministry of Education， Yangtze University， Jingzhou 434023, China
  • Online:2010-09-15 Published:2010-09-15

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Abstract:

Seismic attributes contain abundant geophysical information. There are so many seismic attributes and the relationship between attributes and reservoir characteristics is complicated. Single attribute analysis cannot assure the prediction accuracy. Artificial neural network technology has strong nonlinearmapping ability, so it can be applied to improve hydrocarbon prediction accuracy. The gradient descent learning algorithm is used in neural network. It can avoid local minimum and effectively speed up the network convergence to achieve the global optimal network and improve its forecasting performance.

Key words: seismic-petroleum accumulation science, low saturation lithologic gas reservoir, seismic-petroleum accumulation unit, rock physics modeling

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