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地质统计学反演技术在薄储层预测中的应用
引用本文:王香文,刘红,滕彬彬,王连雨.地质统计学反演技术在薄储层预测中的应用[J].石油与天然气地质,2012,33(5):730-735.
作者姓名:王香文  刘红  滕彬彬  王连雨
作者单位:1. 中国石化 石油勘探开发研究院, 北京 100083; 2. 中国石油 大庆油田有限责任公司 采油一厂, 黑龙江 大庆 163001
摘    要:以厄瓜多尔南部H-N油田M1层薄储层为例,阐述了研究区M1层储层预测难点和存在问题,提出针对性的储层预测方法技术。经过储层和围岩地球物理特征分析,论证了储层预测条件,确定了运用以地质统计学反演为核心的储层预测技术对该区进行储层预测研究,来解决该区储层薄(1~25 ft)、横向变化大、隐蔽性强的薄储层的识别;通过以地震、稀疏脉冲反演、地质统计学反演不同数据体间砂体进行对比分析,精细解释出该区砂体分布;经过无井、盲井和新钻井校验,实现了薄层的高精度预测,提高了预测精度(垂向分辨率达到5 ft)。该预测结果经过在H-N油田的实际应用和新钻井钻探证实,砂层钻遇率为100%,钻探符合率达82%,实现了该区新井产能的突破。

关 键 词:薄储层  地质统计学反演  储层预测  
收稿时间:2012-04-20

Application of geostatistical inversion to thin reservoir prediction
Wang Xiangwen , Liu Hong , Teng Binbin , Wang Lianyu.Application of geostatistical inversion to thin reservoir prediction[J].Oil & Gas Geology,2012,33(5):730-735.
Authors:Wang Xiangwen  Liu Hong  Teng Binbin  Wang Lianyu
Affiliation:1. Exploration and Production Research Institute, SINOPEC, Beijing 100083, China; 2. The First Oil Production Factory of Daqing Oilfield Company, PetroChina, Daqing, Heilongjiang 163001, China
Abstract:Taking M1 thin reservoir in H-N oilfield,Southern Ecuador,as an example,this paper documents the challenges and problems of thin reservoir prediction and presents relevant techniques and methods to tackle these problems.Based on analysis of geophysical characteristics of reservoirs and surrounding rocks,a geostatistical inversion technique is applied in this case to identify the thin(1-25 ft)reservoirs with rapid lateral changes and strong concealment.Sand distribution is refined through correlation between different data volume including seismic interpretation,CSSI(Constrained Sparse Spike Inversion)and geostatistical inversion,and is further checked by non-well,random-wells and newly drilled wells.The accuracy of thin reservoir prediction is greatly enhanced to a vertical resolution up to 5 ft.This technique is successfully applied in H-N oilfield and the new drilling data show that all the prediceted thin sand layers are encountered and the drilling coincidence rate is 82%.
Keywords:thin reservoir  geostatistical inversion  reservoir prediction  
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