期刊文献+

基于椭圆基函数动态模糊神经网络的储层特征预测 被引量:1

Application of Dynamic Fuzzy Neural Networks Based on Elliptical Basis Function to Reservoir Characteristics Prediction
下载PDF
导出
摘要 储层流动单元指数(FZI)能够从岩石物理相的角度体现出孔渗关系,可作为孔渗关系分析的辅助性参数。为了在孔隙度、渗透率未知的情况下对逐个采样点求取FZI,在分析泌阳凹陷白云岩分布区关键井的岩心数据和多种测井资料的基础上,建立了一种基于椭圆基函数(Ellipse Basis Function)的模糊神经网络FZI预测模型,该预测系统可根据学习样本自行创建或删减模糊规则。测井资料信息量庞大,因此这种具有自学习机制的预测系统有利于有效信息的提取和利用,特别对于复杂储层而言,减轻了预测过程中对先验信息的依赖程度,因而效率和精度更高。 Flow zone index (FZI) can reflects the relationship between porosity and permeability from the point of view of the petrophysical phase. Therefore, it can be used as a auxiliary analysis parameter of the relationship of porosity and permeability. In order to calculate FZI point-by-point under conditions of unknown porosity and perme- ability, a fuzzy neural network prediction model based on ellipse basis function was established on the basis of the analysis of the core data and a variety of logging data of Biyang dolostonc reservoir. This prediction system can cre- ate or delete fuzzy rules by analyzing samples. The information contained in the log data is enormous. By using this prediction system with self-learning mechanism, the extraction and utilization of information is more effective. Prac- tical application shows that the accuracy of identification is high. Especially for complex reservoirs, the application of this fuzzy neural networks on reservoir characteristic parameters prediction improves the precision of prediction results and reduces the dependency on prior informations.
出处 《科学技术与工程》 北大核心 2013年第16期4518-4523,4528,共7页 Science Technology and Engineering
基金 中国石油重大科技专项(2011E-0305)资助
关键词 流动单元指数 模糊神经网络 椭圆基函数 测井 flow zone index fuzzy neural networks ellipse basis function well log
  • 相关文献

参考文献10

  • 1Amaefule J O, Altunbuy M. Enhanced reservoir description : using core and log data to identify hydraulic (flow)units and predict permeability in uncored intervals/well. SPE 26436,1993.
  • 2赵军,江同文,王焕增,焦翠华,杨林.基于流动单元的碳酸盐岩渗透率建模方法[J].天然气工业,2007,27(2):46-48. 被引量:6
  • 3朱雪龙.应用信息理论基础[M].北京:清华大学出版社,2000..
  • 4Takagi T, Sugeno M. Fuzzy identification of systems and its applica- tions to modeling and control. IEEE Trans Syst Man Cybem, 1985; 15:116-132.
  • 5Wu Shiqian, Meng Jooer. Dynamic fuzzy neural net works- a novel approach to function approximation. IEEE Transactions On Fuzzy Sys- tems, Man and Cybcmetics, 2000;30 (2) : 354-358.
  • 6Jang J-S R, Sun C T. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans Neural Network, 1993 ; 4:156-158.
  • 7Lin Y H, Cunningham G A. A new approach to fuzzy-neural system modeling. IEEE Trans. Fuzzy Systems, 1995 ;3:190-197.
  • 8Lee S, Kil R M. A Gaussian Potential Function Network with Hierar- chically Self-Organiaing Learning. Neural Networks, 1991; 4: 207-224.
  • 9Lee C C. Fuzzy logic in control systems:fuzzy logic controller Part Ⅰ, Ⅱ. IEEE Trans Syst, Man and Cybem, 1990 ; 20:404--436.
  • 10Chen S, Cowan C F N, Grant P M. Orthogonal least sruares learn- ing algorithm for radial basis function network. IEEE Trans. Neural Networks, 1991 ; 2:302-309.

二级参考文献8

共引文献6

同被引文献10

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部