地震多属性变换法及其在孔隙度预测中的应用——以束鹿凹陷西斜坡台家庄区块为例
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摘要
应用地震资料进行井间储层孔隙度预测是当前油气勘探开发中常用的方法,但通常采用地震反演速度进行孔隙度定量预测难以准确反映孔隙度的空间变化。采用逐步回归和神经网络相结合的地震多属性变换法建立孔隙度模型,可实现孔隙度的空间预测,其核心是:首先通过逐步回归和交叉验证技术来确定最优地震属性及其数量,其次应用神经网络建立所选属性与测井解释孔隙度之间的映射关系,最终反演出孔隙度数据体,进而预测孔隙度的空间变化。以冀中坳陷束鹿凹陷西斜坡台家庄区块沙河街组二段(Es_2)为例,对该方法进行了分析验证。以交叉验证法作为质量控制手段,反演出沙河街组二段孔隙度数据体,直观地反映了沙河街组二段高孔隙度砂岩的展布特征。
The utilization of seismic data in crosswell reservoir porosity prediction is a normal method in current hydrocarbon exploration and development.However,the application of seismic inversion velocity for quantitatively predicting porosity cannot accurately reflect the spatial alternation of porosity.Therefore,we adopted seismic multi-attribute transformation method which combined stepwise regression and neural network to build porosity model and realize the spatial prediction of porosity.Firstly,optimal seismic attributes and its quantity are determined by stepwise regression and cross-validation method.Then,the mapping relation between the optimal seismic attributes and logging porosity is established by neural network. Finally,the porosity of data volume was inverted and its spatial distribution was predicted.Taking the second member of Shahejie Formation in Taijiazhuang Block of Taijiazhuang block of West Slope of Shulu Sag for an example,we applied the method on the data with cross-validation method as quality controlling method to invert the porosity of the interval.The data volume can directly reflect the distribution characteristics of high porosity sandstone in the 2nd Member of the Shahejie Formation.
The utilization of seismic data in crosswell reservoir porosity prediction is a normal method in current hydrocarbon exploration and development.However,the application of seismic inversion velocity for quantitatively predicting porosity cannot accurately reflect the spatial alternation of porosity.Therefore,we adopted seismic multi-attribute transformation method which combined stepwise regression and neural network to build porosity model and realize the spatial prediction of porosity.Firstly,optimal seismic attributes and its quantity are determined by stepwise regression and cross-validation method.Then,the mapping relation between the optimal seismic attributes and logging porosity is established by neural network. Finally,the porosity of data volume was inverted and its spatial distribution was predicted.Taking the second member of Shahejie Formation in Taijiazhuang Block of Taijiazhuang block of West Slope of Shulu Sag for an example,we applied the method on the data with cross-validation method as quality controlling method to invert the porosity of the interval.The data volume can directly reflect the distribution characteristics of high porosity sandstone in the 2nd Member of the Shahejie Formation.
引文
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[13]孔炜,彭苏萍,霍全明,等.地震属性预测测井响应的研究与应用[J].煤炭学报,2004,29(1):45-48
[14]谢雄举,季玉新.优选地震属性预测储层参数的方法及其应用[J].石油物探,2004,43(增刊):127-131
[15]乐友喜,王永刚,张军华.由地震属性向储层参数转换的综合效果分析[J].石油物探,2002,41(2): 202-206
[16]宫健,许淑梅,马云,等.基于地震属性的储层预测方法——以永安地区永3区块沙河街组二段为例[J].海洋地质与第四纪地质,2009,29(6):96-102
[17]石双虎,何樵登,滕吉文,等.利用多元地震属性预测测井特性[J].海洋地质与第四纪地质,2006,28(4): 309-313
[18]张喜,乔向阳,王玉艳,等.神经网络多属性分析技术[J].油气田地面工程,2007,26(8):1-3
[19]Hampson D P,Schuelke J S,Quirein J A.Use of multiattribute transforms to predict log properties from seismic data[J].Geophysics,2001,66(1 ): 220-236
[2]张亚敏,张书法,钱利.地震资料反演砂岩孔隙度方法[J].石油物探,2008,47(2):136-140
[3]Dolberg D M,Helgesen J.Porosity prediction from seismic inversion,Lavrans Field,Halten Terrace, Norway[J].The Leading Edge,2000,19(4): 392-399
[4]马如辉,张平.利用波阻抗反演预测地层孔隙率[J].石油地球物理勘探,2002,37(5):537-540
[5]王长城,施泽进,韩小俊.川南官渡构造下沙溪庙组储层孔隙度预测[J].成都理工大学学报(自然科学版),2008,35(5):508-511
[6]徐旺林,庞雄奇,高仁安.储层测井特征属性反演方法[J].天然气工业,2005,25(3):52-54
[7]Chen Q,Sidney S.Seismic attribute technology for ??reservoir forecasting and monitoring[J].The Leading Edge,1997,16(5):445-456
[8]季玉新,欧钦.优选地震属性预测储层参数方法研究及应用[J].石油地球物理勘探,2003,38(增刊): 57-62
[9]高林,杨勤勇.地震属性技术的新进展[J].石油物探,2004,43(增刊):10-16
[10]周家雄,王立峰,马光克.DF气田储层孔隙度横向预测[J].中国海上油气,2004,16(6):369-386
[11]于兴河.油气储层表征与随机建模的发展历程及展望[J].地学前缘,2008,15(1):1 15
[12]朱玉波,陆光辉,李琳,等.地震属性优化提取及实例分析[J].石油物探,2004,43(增刊):91-93
[13]孔炜,彭苏萍,霍全明,等.地震属性预测测井响应的研究与应用[J].煤炭学报,2004,29(1):45-48
[14]谢雄举,季玉新.优选地震属性预测储层参数的方法及其应用[J].石油物探,2004,43(增刊):127-131
[15]乐友喜,王永刚,张军华.由地震属性向储层参数转换的综合效果分析[J].石油物探,2002,41(2): 202-206
[16]宫健,许淑梅,马云,等.基于地震属性的储层预测方法——以永安地区永3区块沙河街组二段为例[J].海洋地质与第四纪地质,2009,29(6):96-102
[17]石双虎,何樵登,滕吉文,等.利用多元地震属性预测测井特性[J].海洋地质与第四纪地质,2006,28(4): 309-313
[18]张喜,乔向阳,王玉艳,等.神经网络多属性分析技术[J].油气田地面工程,2007,26(8):1-3
[19]Hampson D P,Schuelke J S,Quirein J A.Use of multiattribute transforms to predict log properties from seismic data[J].Geophysics,2001,66(1 ): 220-236
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