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断阶型潜山带地质模型的建立及储层预测--以CG20潜山为例
引用本文:姜素华,王永诗,张家震,王军.断阶型潜山带地质模型的建立及储层预测--以CG20潜山为例[J].中国海洋大学学报(自然科学版),2004,34(3):437-445.
作者姓名:姜素华  王永诗  张家震  王军
作者单位:1. 石油大学,山东,东营,257061;中国海洋大学,山东,青岛,266003
2. 中国石化胜利油田地质科学研究院,山东,东营,257015
3. 中国石化胜利油田物探研究院,山东,东营,257015
基金项目:国家“十五”重大科技攻关项目 ( 2 0 0 1 BA60 5 A0 9)资助
摘    要:以 CG2 0潜山为例 ,从建立地质模型入手 ,包括地层模型、构造模型、储集模型、储盖组合模型、速度模型等 ,认识到各套地层分布和储层物性的差异均与地震响应密切相关 ,因此可以利用地震波的信息 ,预测潜山储层的发育及分布情况。在对 CG2 0潜山进行精细全三维构造解释的基础上 ,探讨性地应用了测井约束反演、吸收系数、相干分析及三维模式识别等技术 ,对潜山储层进行了预测 ,从而提高了潜山勘探的效益 ,并为类似断阶型潜山带的勘探提供了成功的经验 ,具有一定的指导意义。

关 键 词:断阶型潜山带  地质模型  测井约束反演  三维模式识别
文章编号:1672-1574(2004)03-437-09
修稿时间:2003年5月28日

Establishment of Geological Models of Terrace Fault Buried Hill and Reservoir Prediction:with the CG20 Buried Hill As An Example
JING Su-hua ,WANG Yong-shi ,ZHANG Jia-zhen ,WANG Jun.Establishment of Geological Models of Terrace Fault Buried Hill and Reservoir Prediction:with the CG20 Buried Hill As An Example[J].Periodical of Ocean University of China,2004,34(3):437-445.
Authors:JING Su-hua    WANG Yong-shi  ZHANG Jia-zhen  WANG Jun
Institution:JING Su-hua 1,2,WANG Yong-shi 3,ZHANG Jia-zhen 3,WANG Jun 4
Abstract:Taking CG20 buried hill as an example, through the establishment of geological models, including a stratum model, a structural model, a reservoir model,a reservoir -seal combination model, a velocity model, etc., it is learnt that the seismic response has a close relation to the distribution of the layers and the property difference of the reservoir layer. Therefore the development and distribution of the buried hill reservoir can be predicted. Based on fine, full 3-D structural interpretations of the CG20 buried hill, the technologies of well-log-constraint inversion,absorption coefficient,coherency analysis and 3-D pattern recognition are applied to predict the reservoir. With this approach the economic benefit of the buried hill exploration is enhanced and it can be helpful for the exploration of similar terrace fault buried hill zones.
Keywords:terrace fault buried hill zone  geological model  well-log-constraint inversion  3-D pattern recognition
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