石油与天然气地质 ›› 2000, Vol. 21 ›› Issue (2): 173-176.doi: 10.11743/ogg20000220

• 技术经纬 • 上一篇    下一篇

基于神经模糊系统的储层参数反演

杨斌, 肖慈珣, 王斌, 彭真明   

  1. 成都理工学院信息工程与地球物理系,四川成都610059
  • 收稿日期:2000-01-11 出版日期:2000-06-25 发布日期:2012-01-16

RESERVOIR PARAMETER INVERSION USING NEURAL FUZZY SYSTEM

Yang Bin, Xiao Cixun, Wang Bin, Peng Zhenming   

  1. Department of Imformation Engineering and Geophysics, Chengdu College of Technology, Chengdu, Sichuan
  • Received:2000-01-11 Online:2000-06-25 Published:2012-01-16

PDF (PC)

480

被引次数

74

摘要：

神经模糊系统,即把神经网络与模糊逻辑结合起来,用神经网络来构造模糊系统,使建立的储层参数反演模型既能处理输入信息,又能嵌入专家的模糊性知识,提高了模型的抗干扰能力和预测精度,同时,也克服了人工神经网络技术的储层参数反演与预测在实际应用中暴露出的一些难点。通过基于神经模糊系统的网络结构、建模步骤及应用的研究,与常规多元线性回归分析、模糊系统建模相比较,获得了较高的储层参数反演预测精度。

关键词: 储层参数, 神经网络, 模糊逻辑, 反演预测, 应用

Abstract:

Although a lot of developments have been gained on the reservoir parameters inversion with the artificial neural Networks(ANN),but the problem of the finite training data has been exist for a long time,and ANN is difficult to handle fuzzy expert knowledge.In order to make full use of expert knowledge,a nonlinear intelligent inversion approach based on neural networks driven fuzzy reasoning(NNFR)system is presented in this paper to achieve the ability of self learning,self adapation and fuzzy information input.With some practical application of proposed methods,much better results of reservoir parameter inversion from analysis data are obtained.

Key words: reservoir parameter, neural networks, fuzzy logic, inversion prediction, application

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