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基于粗糙集理论的超稠油油藏水平井吞吐效果评价及其影响因素分析
引用本文:杜殿发,李冬冬,石达友,王青.基于粗糙集理论的超稠油油藏水平井吞吐效果评价及其影响因素分析[J].数学的实践与认识,2010,40(17).
作者姓名:杜殿发  李冬冬  石达友  王青
摘    要:影响超稠油油藏水平井蒸汽吞吐开发效果的因素包括地质参数、流体参数、开发参数、水平井设计参数等众多因素,寻找主要因素对于超稠油油藏水平井吞吐效果评价及开发调整设计具有重要意义.基于粗糙集理论对影响超稠油油藏水平井吞吐效果的10个因素进行数据属性约简,同时利用灰色关联分析,对这些因素进行敏感性分析;应用Elman神经网络比较上述两种结果,证明了粗糙集理论进行数据属性约简的方法更准确.最终得到注汽干度、注汽强度、注汽速度、水平段长度和注汽压力对水平井的开发效果影响最大.实例计算表明,方法实用有效,精度较高,可用于超稠油油藏水平井吞吐效果评价及开发、调整设计.

关 键 词:水平井  蒸汽吞吐  粗糙集理论  数据属性约简  灰色关联分析  Elman神经网络

Analysis and Evaluation of Influencing Factors of Horizontal Well in Super Heavy Oil Reservoir Based on Rough Set Theory
DU Dian-fa,LI Dong-dong,SHI Da-you,WANG Qing.Analysis and Evaluation of Influencing Factors of Horizontal Well in Super Heavy Oil Reservoir Based on Rough Set Theory[J].Mathematics in Practice and Theory,2010,40(17).
Authors:DU Dian-fa  LI Dong-dong  SHI Da-you  WANG Qing
Abstract:Factors influencing the huff and puff development effect of horizontal well in super heavy oil reservoir include geological parameters,fluid parameters,development parameters, horizontal well designing paramenters and so on.Finding out the primary influencing factors is very important to the huff and puff effect evaluation and development adjustment design. Based on rough set theory,five key factors have been picked out from ten common factors and the parameters sensitivity has been also analyzed with grey association analysis.The calculation accuracy of the two methods was compared by Elman neural network.It has been proved that the method of data attribute reduction based on rough set theory is more accurate.According to the results,the steam quality,steam injection intensity,steam injection velocity,steam injection pressure and the length of horizontal well are most important factors.Calculation of an example has shown that the effective and accurate method can be applied to the huff and puff effect evaluation,development and adjustment design.
Keywords:horizontal well  huff and puff  rough set theory  data attribute reduction  grey association analysis  Elman neural network
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