节点文献

利用地质统计学反演进行薄砂体储层预测

THE APPLICATION OF GEOSTATISTIC INVERSION METHOD TO PREDICTING THE THIN SANDSTONE RESERVOIR

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 何火华李少华杜家元李密王全

【Author】 HE Huo-hua1,2,LI Shao-hua1,DU Jia-yuan3,LI Mi1,Wang Quan2(1.MOE Key Laboratory of Oil & Gas Resources and Exploration Technique,Yangtze University,Jingzhou 434023,China;2.The Zhiluo Oil Production Plant of Yanchang Oil Field Co.,Ltd.,Yanan 727500,China;3.The Shenzhen Branch Institute,CNOOC,Guangzhou 510240,China)

【机构】 长江大学油气资源与勘探技术教育部重点实验室延长油田股份有限公司直罗采油厂中海石油有限公司深圳分公司研究院

【摘要】 某地区三角洲前缘砂体相变快,非均质性强,单砂体厚度小,常规的确定性反演由于受到地震频带限制,反演地震体的垂向分辨力低,往往难以识别。基于随机建模技术的地质统计学反演方法,能有效地综合地质、测井和三维地震数据,极大地提高了预测结果的垂向分辨率,能更加精确地描述储层细微的变化,可以更好地识别薄层砂体。利用地质统计学反演方法对某地区三角洲前缘目标层段的薄层砂体进行了预测,通过高分辨率的地质统计学反演波阻抗体和密度反演体剖面对比,可以有效识别出薄层砂体在反演剖面上横向展布。

【Abstract】 The delta-front of a certain area is characterized by fast facies change,strong heterogeneity,and small thickness of a single sand body.As the earthquake-band of the conventional deterministic inversion is limited,the vertical resolution of the seismic inversion body is very low,and hence it is often difficult to identify the small sand body.The geostatistic inversion method based on stochastic modeling technique can effectively integrate geological,logging and seismic data,greatly improve the seismic vertical resolution,and more accurately describe the subtle changes in the reservoir,thus well identifying the thin sand.Geostatistic Inversion was used in delta-front of a target area to predict the thin sand layers.The impedance body derived from the high-resolution geostatistic inversion method was compared with the density body,which could effectively identify a thin layer of sand body in the horizontal distribution on the inversion profile.This method provides a basis for identifying favorable oil and gas traps.

【基金】 国家科技重大专项(2011ZX05023-002);湖北省自然科学基金项目(2010CDB04302)资助
  • 【文献出处】 物探与化探 ,Geophysical and Geochemical Exploration , 编辑部邮箱 ,2011年06期
  • 【分类号】P628.2;P618.13
  • 【被引频次】52
  • 【下载频次】796
节点文献中: 

本文链接的文献网络图示:

本文的引文网络