[1]伍泽云,陈振标,王晓光,等.自适应BP神经网络技术在超低渗储层分类中的应用[J].测井技术,2009,33(06):544-549.
 WU Ze-yun,CHEN Zhen-biao,WANG Xiao-guang,et al.Application of Self-adapted Neural Networks Technology to Extra Low Permeability Reservoirs Classification[J].WELL LOGGING TECHNOLOGY,2009,33(06):544-549.
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自适应BP神经网络技术在超低渗储层分类中的应用()
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《测井技术》[ISSN:1004-1338/CN:61-1223/TE]

卷:
第33卷
期数:
2009年06期
页码:
544-549
栏目:
处理解释
出版日期:
2009-12-30

文章信息/Info

Title:
Application of Self-adapted Neural Networks Technology to Extra Low Permeability Reservoirs Classification
作者:
伍泽云;陈振标;王晓光;苏静;
长江大学油气资源与勘探技术教育部重点实验室;胜利油田地质科学研究院;新疆油田公司勘探开发研究院;
Author(s):
WU Ze-yun1CHEN Zhen-biao2WANG Xiao-guang1SU Jing3
1.Key Laboratory of Exploration Technologies for Oil and Gas Resources,Ministry of Education,Yangtze University,JingzhouHubei 434023,China;2.Geological Scientific Research Institute,Shengli Oilfield,Dongying,Shandong 257015,China;3.Exploration and Development Research Institute of Xinjiang Oilfield Company,Karemary,Xinjiang 834000,China
关键词:
测井应用储层分类神经网络低孔隙度低渗透率长6段
分类号:
P631.84
摘要:
利用研究区内22块压汞样品及4440块有效物性分析样品,建立起超低渗透率储层的分类标准。通过与前人研究成果的比较,发现所建立的标准是可行的。借助自适应BP神经网络技术建立了适合研究区长6段的储层分类判别模型,并将其判别结果与综合判别结果对比,吻合程度较高。

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更新日期/Last Update: 2009-12-30