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地质统计学反演及其在吉林扶余油田储层预测中的应用
引用本文:孙思敏,彭仕宓. 地质统计学反演及其在吉林扶余油田储层预测中的应用[J]. 物探与化探, 2007, 31(1): 51-54,
作者姓名:孙思敏  彭仕宓
作者单位:中国石油大学,北京,102249
摘    要:地质统计学反演方法将随机建模技术与常规地震反演相结合,有效地综合地质、测井和三维地震数据,可以更加精确地描述储层的变化.在执行地质统计学反演前,首先应用稀疏脉冲约束反演,了解储层的大致分布,以求取子波和水平变差函数.地质统计学反演从井点出发,井间以原始地震数据作为硬数据,通过随机模拟的产生井间波阻抗,然后将波阻抗转换成反射系数,并用确定性反演方法求得的子波褶积产生地震道,通过反复迭代直至合成地震道与原始地震数据达到一定程度的匹配,反演结果是多个等概率的波阻抗数据体实现.反演结果符合输入数据的地质统计学特征并受地质模型的约束,它综合了测井的垂向分辨率高和地震的横向分辨率高的优势,结果的多个实现用于不确定性评价.

关 键 词:吉林扶余油田  地质统计学反演  随机模拟  储层预测
文章编号:1000-8918(2007)01-0051-04
收稿时间:2006-06-16
修稿时间:2006-06-16

GEOSTATISTICAL INVERSION METYOD AND ITS APPLICATION TO RESERVOIR PREDICITION OF FUYU OILFIELD IN JILIN PROVINCE
SUN Si-min,PENG Shi-mi. GEOSTATISTICAL INVERSION METYOD AND ITS APPLICATION TO RESERVOIR PREDICITION OF FUYU OILFIELD IN JILIN PROVINCE[J]. Geophysical and Geochemical Exploration, 2007, 31(1): 51-54,
Authors:SUN Si-min  PENG Shi-mi
Affiliation:China University of Petroleum, Beijing 102249, China
Abstract:Geostatistical inversion(GI) combines the advantages of stochastic modeling and seismic inversion,integrates geological,logging and seismic data,and delineates the reservoir more preciously.Constrained sparse spike inversion is conducted to predict the general distribution of reservoirs before GI,and the results including wavelet and 3D acoustic impedance(AI) can be used later.The inversion uses both well-log and seismic data.The inter-well AI is created by stochastic simulation,and the subsequent work includes(1) simulating an AI at the interwell point,followed by computing reflectivity series,(2) convolving it with the wavelet selected to simulate a local trace,and(3) implementing iterations to make the simulated and seismic trace reach a desired fit.The results are of multi-equiprobable 3D AI volume realization,which can be used for uncertainty evaluation.The inversion integrates the high resolution of logging and lateral directions of the seismic data.The results fit the geostatistical features of the input data,and are constrained by structural and stratigraphic earth models.
Keywords:Geostatistical inversion    seismic inversion   stochastic simulation
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