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碳酸盐岩孔隙结构参数构建与储层参数反演(英文)
引用本文:蒋炼,文晓涛,周东红,贺振华,贺锡雷.碳酸盐岩孔隙结构参数构建与储层参数反演(英文)[J].应用地球物理,2012,9(2):223-232.
作者姓名:蒋炼  文晓涛  周东红  贺振华  贺锡雷
作者单位:中国海油(中国)有限公司天津分公司;成都理工大学油气藏地质及开发国家重点实验室
基金项目:sponsored by the National Nature Science Foundation of China (Grant No.40904034 and 40839905)
摘    要:碳酸盐岩储层孔隙结构相对碎屑岩更复杂,常用的岩石物理模型不能较好的描述其孔隙结构的变化规律,且岩石孔隙结构的差异较大程度上会影响岩石的弹性性质。本文首先利用岩石薄片分析了碳酸盐岩的微观孔隙结构。然后基于Gassmann方程和Eshelby-Walsh椭球包体裂缝理论,在合理的假设前提下给出了一种新的岩石物理建模方法,并且从中提取了一个参数来表征孔隙结构的变化规律。最后,基于全波列测井数据,我们利用该方法计算了单井的孔隙度,并与用常规方法预测的结果进行了比较,同时进行了地震储层参数反演。研究结果表明,孔隙结构对岩石的弹性性质的影响较大,且新的建模方法预测的孔隙度误差仅为0.74%。因此,该方法可有效的减小孔隙结构对计算各岩石弹性参数的影响并提高孔隙度的预测精度。

关 键 词:碳酸盐岩  岩石物理模型  孔隙结构参数  储层参数反演

The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters
Lian Jiang,Xiao-Tao Wen,Dong-Hong Zhou,Zhen-Hua He,Xi-Lei He.The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters[J].Applied Geophysics,2012,9(2):223-232.
Authors:Lian Jiang  Xiao-Tao Wen  Dong-Hong Zhou  Zhen-Hua He  Xi-Lei He
Institution:1. China National Offshore Oil Corporation (CNOOC) Ltd Tian Jin Branch, Tianjin, 300452, China
2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, 610059, China
Abstract:With a more complex pore structure system compared with clastic rocks, carbonate rocks have not yet been well described by existing conventional rock physical models concerning the pore structure vagary as well as the influence on elastic rock properties. We start with a discussion and an analysis about carbonate rock pore structure utilizing rock slices. Then, given appropriate assumptions, we introduce a new approach to modeling carbonate rocks and construct a pore structure algorithm to identify pore structure mutation with a basis on the Gassmann equation and the Eshelby-Walsh ellipsoid inclusion crack theory. Finally, we compute a single well??s porosity using this new approach with full wave log data and make a comparison with the predicted result of traditional method and simultaneously invert for reservoir parameters. The study results reveal that the rock pore structure can signicantly influence the rocks?? elastic properties and the predicted porosity error of the new modeling approach is merely 0.74%. Therefore, the approach we introduce can effectively decrease the predicted error of reservoir parameters.
Keywords:Carbonate rocks  rock physical model  pore structure algorithm  reservoir parameter inversion
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