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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 | |
| 引用本文： | 伍泽云,陈振标,王晓光,苏静.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(6):544-549. |
| 作者姓名： | 伍泽云 陈振标 王晓光 苏静 |
| 作者单位： | 1. 长江大学油气资源与勘探技术教育部重点实验室,湖北,荆州,434023 2. 胜利油田地质科学研究院,山东,东营,257025 3. 新疆油田公司勘探开发研究院,新疆,克拉玛依,834000 |
| 基金项目： | 国家863项目06Z2课题，中国石油集团公司项目 |
| 摘 要： | 利用研究区内22块压汞样品及4 440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. |
| 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 |
Application of Self-adapted Neural Networks Technology to Extra Low Permeability Reservoirs Classification |
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| WU Ze-yun,CHEN Zhen-biao,WANG Xiao-guang,SU Jing.Application of Self-adapted Neural Networks Technology to Extra Low Permeability Reservoirs Classification[J].Well Logging Technology,2009,33(6):544-549. | |
| Authors: | WU Ze-yun CHEN Zhen-biao WANG Xiao-guang SU Jing |
| Affiliation: | WU Ze-yun1,CHEN Zhen-biao2,WANG Xiao-guang1,SU Jing3(1.Key Laboratory of Exploration Technologies for Oil , Gas Resources,Ministry of Education,Yangtze University,JingzhouHubei 434023,China,2.Geological Scientific Research Institute,Shengli Oilfield,Dongying,Sh,ong 257015,3.Exploration , Development Research Institute of Xinjiang Oilfield Company,Karemary,Xinjiang 834000,China) |
| Abstract: | Based on 22 mercury injection samples and 4440 effective physical properties analysis samples,established is a classification standard of extra low permeability reservoirs.After comparing it with former results,we find our standard is practical.Meanwhile self-adapted neural networks technology is used to establish reservoir classification model in Chang 6 block,and its results are close to the comprehensive analysis results. |
| Keywords: | log application reservoir classification neural-network low porosity low permeability Chang 6 block |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |
