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储层随机建模方法在细分沉积相中的应用
引用本文:汤军,宋树华,徐论勋,肖传桃,赵金玲,雷正军.储层随机建模方法在细分沉积相中的应用[J].天然气地球科学,2007,18(1):89-92.
作者姓名:汤军  宋树华  徐论勋  肖传桃  赵金玲  雷正军
作者单位:1. 中国地质大学地球科学与资源学院,北京,100083;油气资源与勘探技术教育部重点实验室(长江大学),湖北,荆州,434023;长江大学地球科学学院,湖北,荆州,434023
2. 油气资源与勘探技术教育部重点实验室(长江大学),湖北,荆州,434023;长江大学地球科学学院,湖北,荆州,434023
3. 长江大学地球科学学院,湖北,荆州,434023
4. 新疆克拉玛依新疆油田公司数据中心,新疆,克拉玛依,834000
5. 吉林石油集团,实验室,长江大学,湖北,荆州,434023
摘    要:以SN油田S109井区油藏为研究对象,在储层构造、沉积微相、测井解释、储层非均质等研 究基础上,建立地质数据库,应用三维可视化技术对油藏构造、沉积微相和油藏属性建立三 维模型,通过建立的储层三维模型,对沉积微相划分等地质工作进行了检验和修改,从而建 立起符合研究区的沉积微相及储层物性分布,为后续油藏数值模拟工作打下基础。

关 键 词:储层随机建模  沉积相  构造模型  属性模型  指示模拟方法
文章编号:1672-1926(2007)01-0089-04
收稿时间:2006-07-20
修稿时间:2006-07-202006-12-11

APPLICATION OF THE RESERVOIR STOCHASTIC MODELING IN SUBDIVIDING SEDMENTARY FACIES
TANG Jun,SONG Shu-hua,XU Lun-xun,XIAO Chuan-tao,ZHAO Jin-ling,LEI Zheng-jun.APPLICATION OF THE RESERVOIR STOCHASTIC MODELING IN SUBDIVIDING SEDMENTARY FACIES[J].Natural Gas Geoscience,2007,18(1):89-92.
Authors:TANG Jun  SONG Shu-hua  XU Lun-xun  XIAO Chuan-tao  ZHAO Jin-ling  LEI Zheng-jun
Affiliation:1. School of Earth Sciences and Resources, China University of Geosciences , Beijing 100083, China; 2. Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University
Abstract:This paper introduces a 3D geological modeling technology of checking out and guiding geologic works. The technology can guarantee the middle and late stage development of oilfields, and can enhance the accuracy of the reservoir geological modeling. Based on the study of reservoir structures, sedimentary microfacies, interpretation of logging data, and reservoir heterogeneity, a 3D visualized geological model of the S109 section of SN Oilfield is established using the technology of 3D visualized geological modeling and the geologic database. The result is used in the reservoir precise digital modeling and in examining or amending the foundational work such as the sedimentary facies. The technology improves the precision of reservoir characterization and shortens the study period.
Keywords:Stochastic modeling of reservoir  Sedimentary facies  Structure model  Petrophysics model  Indication simulation  
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