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基于地质统计理论的储集层潜在伤害钻前预测方法
引用本文:张锐,王瑞和,于世花,邱正松,徐守余.基于地质统计理论的储集层潜在伤害钻前预测方法[J].地球物理学报,2009,52(6):1657-1662.
作者姓名:张锐  王瑞和  于世花  邱正松  徐守余
作者单位:1.中国石油大学石油工程学院,东营 257061;2.中国石油大学图书馆,东营 257061;3.中国石油大学地球资源与信息学院,东营 257061
基金项目:中国石油天然气集团公司创新基金项目 
摘    要:储集层潜在伤害预测研究是各项作业方案设计的依据.应用地质统计的区域化变量理论和其估计、模拟处理方法,针对钻前情况,在储集层潜在伤害空间特性分析的基础上,建立了储集层潜在伤害钻前预测方法,并以胜利某油田区块Es2地层水敏伤害结果为例进行了钻前预测.结果表明,该方法在钻前情况下给出了该区块的水敏指数分布,且预测分布结果与该区块实际水敏指数分布情况相符,提出的储集层潜在伤害钻前预测方法切实可行.

关 键 词:储集层潜在伤害  钻前预测  水敏  地质统计  区域化变量  
收稿时间:2008-6-13
修稿时间:2008-6-18

Prediction method of formation potential damage in pre-drilling based on geological statistics
ZHANG Rui,WANG Rui-He,YU Shi-Hua,QIU Zheng-Song,XU Shou-Yu.Prediction method of formation potential damage in pre-drilling based on geological statistics[J].Chinese Journal of Geophysics,2009,52(6):1657-1662.
Authors:ZHANG Rui  WANG Rui-He  YU Shi-Hua  QIU Zheng-Song  XU Shou-Yu
Institution:1.Faculty of Petroleum Engineering,China University Petroleum, Dongying 257061, China;2.Library, China University Petroleum, Dongying 257061, China;3.College of Geo-resources and Information, China University Petroleum, Dongying 257061, China
Abstract:Formation potential damage prediction is a useful reference for all kinds of operation scheme designing. According to regionalized variable theory and processing methods, a new method of pre-drilling prediction for formation potential damage was presented based on analysis of spatial character of formation potential damage, in which geological statistics was introduced to describe the character, and employed to conduct pre-drilling prediction of formation potential damage utilizing water sensitivity data of Es2 of a block in Shengli oilfield. The result showed that the new method gave potential water sensitivity distribution of the block under the condition of pre-drilling, and the prediction distribution was in correspondence with actual water sensitivity distribution, and the method of pre-drilling prediction presented here was feasible.
Keywords:Formation potential damage  Pre-drilling prediction  Water sensitivity  Geological statistics  Regionalized variable
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