地震属性分析在河流相储层预测中的应用
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摘要
由河道砂岩体所形成的岩性油气藏的勘探越来越受到勘探家们的重视。但由于河流相储层形成的复杂性和多样性,依靠钻井是很难对其分布规律进行有效控制和预测的。必须充分发挥地震资料信息的横向连续性优势,在井资料的标定下,进行储层的横向预测与研究。通过地震属性分析,对渤海湾盆地冀中拗陷霸县凹陷文安斜坡古渐系沙一上亚段—东营组河流相储层进行了预测。研究表明:(1)地震波的波峰和波谷强振幅分别反映了河道边、心滩砂体的分布及河道水系的发育情况;(2)地震资料四阶导数振幅属性对振幅的微弱变化敏感,能较好地识别河道砂体分布,尤其是对整体河床分布的刻画非常逼真;(3)河道沉积以低频反射为特征,尤以小于10 Hz的频率分布区与河道砂体具有较好的对应关系;(4)基于神经网络的波形分类技术,可以极大地克服单一地震属性参数所带来的多解性或不确定性,当用井资料标定后,其分类预测结果与地质认识具有很高的符合率。
The exploration of lithologic oil pool formed by the channel sand body is getting more and more attention by explorationists.Because of the complexity and diversity of the fluvial reservoir,it is difficult to be controlled and predicated effectively with well data.With the well data’s calibrating,the fluvial reservoir prediction must be made by sufficiently taking advantage of the lateral continuity of seismic data.Wuth the analysis of seismic attribution,the paper presents the fluvial reservoir predicting in the upper section of Es 1 and Ed formation of the Paleogene in Wen’an Slope,Baxian Depression,Jizhong Sag,Bohaiwan Basin.The results are:(1)The high amplitude of peaks and troughs of seismic wave represent the distribution of the fluvial sand body and the water system(.2)The amplitudes fourth derivative is sensitive to the weak change,so it can identify the channel sand body easily,especially for carving the distribution of the channel bed(.3)The channel deposit is characterized by low frequency seismic reflection,and the distribution of fluvial sand body was corresponded with the area of frequency lower than 10 Hz(.4)Based on the waveform analysis technique with neural network,we can overcome the ambiguity and uncertainty brought by single parameter in seismic attribution.Through calibrating with well data,the results of classification highly correspond with the recognition of geology.
The exploration of lithologic oil pool formed by the channel sand body is getting more and more attention by explorationists.Because of the complexity and diversity of the fluvial reservoir,it is difficult to be controlled and predicated effectively with well data.With the well data’s calibrating,the fluvial reservoir prediction must be made by sufficiently taking advantage of the lateral continuity of seismic data.Wuth the analysis of seismic attribution,the paper presents the fluvial reservoir predicting in the upper section of Es 1 and Ed formation of the Paleogene in Wen’an Slope,Baxian Depression,Jizhong Sag,Bohaiwan Basin.The results are:(1)The high amplitude of peaks and troughs of seismic wave represent the distribution of the fluvial sand body and the water system(.2)The amplitudes fourth derivative is sensitive to the weak change,so it can identify the channel sand body easily,especially for carving the distribution of the channel bed(.3)The channel deposit is characterized by low frequency seismic reflection,and the distribution of fluvial sand body was corresponded with the area of frequency lower than 10 Hz(.4)Based on the waveform analysis technique with neural network,we can overcome the ambiguity and uncertainty brought by single parameter in seismic attribution.Through calibrating with well data,the results of classification highly correspond with the recognition of geology.
引文
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[2]朱广生.地震资料储层预测方法[M].北京:石油工业出版社,1995:224–236
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[5]徐伯勋,白旭滨,于常青.地震勘探信息技术(提取、分析和预测)[M].北京:地质出版社,2001:6–21.
[6]盛海波,刘蕊,付国民,等.塔河油田3区石炭系超深薄层碎屑岩储层预测研究[J].地球科学与环境学报,2011,33(4):390–396.ShenHaibo,LiuRui,FuGuomin,etal.StudyonreservoirforecastingforultradeepthinclasticrockofcarboniferoussysteminBlock3ofTaheOilfield[J].JournalofEarthSci-ences and Environment.2011,33(4):390–396.
[7]Chen Q,Steve S.Seismic attribute technology for reser-voir forecasting and monitoring[J].The Leading Edge,1997,16(5):445–456.
[8]Uwe Strecker,Maggie Smith,Richard Uden,et al.Seis-mic attribute analysis in hydrothermal dolomite,devo-nian slave point formation,Northeast British Columbia,Canada[J].SEG Technical Program Expanded Abstracts,2004,23:378–381.
[9]刘文岭,牛彦良,李刚,等.多信息储层预测地震属性提取与有效性分析方法[J].石油物探,2002,41(1):100–106.Liu Wenling,Niu Yanliang,Li Gang,el al.Seismicattribute extraction and effectiveness analysis of mul-ti–attribute reservoir prediction[J].Geophysical Prospect-ing for Petrole.2002,41(1):100–106.
[10]凌云研究组.利用多属性体分类技术预测扇三角洲砂体[J].石油地球物理勘探,2003,38(6):642–653.Ling Yun.Study on application of basie seismic attributesto interpretation of depositional environment[J].Oil Geo-physical Prospecting,2003,38(6):642–653.
[11]董立生.深层浊积砂砾岩体地球物理预测技术研究[J].西南石油大学学报:自然科学版,2010,32(1):52–56.Dong Lisheng.Study on geophysical technology to pre-dict deep turbidite sandstone and conglomerate bodies[J].Journal of Southwest Petroleum University:Science&Technology Edition,2010,32(1):52–56.
[12]郎晓玲,彭仕宓,康洪全,等.利用多属性分类技术预测扇三角洲砂体[J].西南石油大学学报:自然科学版,2010,32(1):57–62.Lang Xiaoling,Peng Shimi,Kang Hongquan,et al.Pre-dict fan delta sand body by using multi-attributes vol-ume classification technology[J].Journal of SouthwestPetroleum University:Science&Technology Edition,2010,32(1):57–62.
[13]郝志伟,王楠,田建华,等.地震反射及属性对沉积相反映程度分析[J].西南石油大学学报:自然科学版,2011,33(5):75–78.Hao Zhiwei,Wang Nan,Tian Jianhua,el al.Analysis ofseismic reflection degree for sedimentary facies in con-tinental basin[J].Journal of Southwest Petroleum Univer-sity:Science&TechnologyEdition,2011,33(5):75–78.
[14]李津,孙渊,张成利.地震属性参数神经网络油气预测应用研究[J].西安石油学院学报,1999,14(6):17–19.LiJin,Sunyuan,ZhangChengli.PredictionofoilandgasbasedonseismicdatabyfuzzyBPneuralnetwork[J].Jour-nal of Xi’an Petroleum Institute,1999,14(6):17–19.
[15]靳军,刘洛夫,李双文,等.用地震属性分析莫8井区J1sJin Jun,Liu Luofu,Li Shuangwen,et al.Study on the mi-crofacies distribution of J1s1/砂体微相展布[J].西安石油大学学报:自然科学版,2009,24(1):41–43.Jin Jun,Liu Luofu,Li Shuangwen,et al.Study on the mi-crofacies distribution of J 1 s 1 2 sandbody in Mo-8wellblock of Jungar Basin by means of the characteristics of seismic attributes[J].Journal of Xi’an Shiyou University:Natural Science Edition,2009,24(1):41–43,51.
[16]Brown A R.地震属性及其分类[J].严又生,译.国外油气勘探,1997,9(4):529–530.
[17]庄锡进,靳军,支东明.Stratimagic软件在准噶尔盆地地震勘探中的应用[J].新疆石油地质,2002,23(3):245–247.Zhuang Xijin,Jin jun,Zhi Dongming.Application of statimagic software in seismic exploration of Junggar Basin[J].Xinjiang Petroleum Geology,2002,23(3):245–247.
[2]朱广生.地震资料储层预测方法[M].北京:石油工业出版社,1995:224–236
[3]王捷.油藏描述技术(勘探阶段)[M].北京:石油工业出版社,1996:176–250.
[4]孙家振,李兰斌.多信息储层预测与油气判别(原理、方法与应用实例)[M].武汉:中国地质大学出版社,1997:58–69.
[5]徐伯勋,白旭滨,于常青.地震勘探信息技术(提取、分析和预测)[M].北京:地质出版社,2001:6–21.
[6]盛海波,刘蕊,付国民,等.塔河油田3区石炭系超深薄层碎屑岩储层预测研究[J].地球科学与环境学报,2011,33(4):390–396.ShenHaibo,LiuRui,FuGuomin,etal.StudyonreservoirforecastingforultradeepthinclasticrockofcarboniferoussysteminBlock3ofTaheOilfield[J].JournalofEarthSci-ences and Environment.2011,33(4):390–396.
[7]Chen Q,Steve S.Seismic attribute technology for reser-voir forecasting and monitoring[J].The Leading Edge,1997,16(5):445–456.
[8]Uwe Strecker,Maggie Smith,Richard Uden,et al.Seis-mic attribute analysis in hydrothermal dolomite,devo-nian slave point formation,Northeast British Columbia,Canada[J].SEG Technical Program Expanded Abstracts,2004,23:378–381.
[9]刘文岭,牛彦良,李刚,等.多信息储层预测地震属性提取与有效性分析方法[J].石油物探,2002,41(1):100–106.Liu Wenling,Niu Yanliang,Li Gang,el al.Seismicattribute extraction and effectiveness analysis of mul-ti–attribute reservoir prediction[J].Geophysical Prospect-ing for Petrole.2002,41(1):100–106.
[10]凌云研究组.利用多属性体分类技术预测扇三角洲砂体[J].石油地球物理勘探,2003,38(6):642–653.Ling Yun.Study on application of basie seismic attributesto interpretation of depositional environment[J].Oil Geo-physical Prospecting,2003,38(6):642–653.
[11]董立生.深层浊积砂砾岩体地球物理预测技术研究[J].西南石油大学学报:自然科学版,2010,32(1):52–56.Dong Lisheng.Study on geophysical technology to pre-dict deep turbidite sandstone and conglomerate bodies[J].Journal of Southwest Petroleum University:Science&Technology Edition,2010,32(1):52–56.
[12]郎晓玲,彭仕宓,康洪全,等.利用多属性分类技术预测扇三角洲砂体[J].西南石油大学学报:自然科学版,2010,32(1):57–62.Lang Xiaoling,Peng Shimi,Kang Hongquan,et al.Pre-dict fan delta sand body by using multi-attributes vol-ume classification technology[J].Journal of SouthwestPetroleum University:Science&Technology Edition,2010,32(1):57–62.
[13]郝志伟,王楠,田建华,等.地震反射及属性对沉积相反映程度分析[J].西南石油大学学报:自然科学版,2011,33(5):75–78.Hao Zhiwei,Wang Nan,Tian Jianhua,el al.Analysis ofseismic reflection degree for sedimentary facies in con-tinental basin[J].Journal of Southwest Petroleum Univer-sity:Science&TechnologyEdition,2011,33(5):75–78.
[14]李津,孙渊,张成利.地震属性参数神经网络油气预测应用研究[J].西安石油学院学报,1999,14(6):17–19.LiJin,Sunyuan,ZhangChengli.PredictionofoilandgasbasedonseismicdatabyfuzzyBPneuralnetwork[J].Jour-nal of Xi’an Petroleum Institute,1999,14(6):17–19.
[15]靳军,刘洛夫,李双文,等.用地震属性分析莫8井区J1sJin Jun,Liu Luofu,Li Shuangwen,et al.Study on the mi-crofacies distribution of J1s1/砂体微相展布[J].西安石油大学学报:自然科学版,2009,24(1):41–43.Jin Jun,Liu Luofu,Li Shuangwen,et al.Study on the mi-crofacies distribution of J 1 s 1 2 sandbody in Mo-8wellblock of Jungar Basin by means of the characteristics of seismic attributes[J].Journal of Xi’an Shiyou University:Natural Science Edition,2009,24(1):41–43,51.
[16]Brown A R.地震属性及其分类[J].严又生,译.国外油气勘探,1997,9(4):529–530.
[17]庄锡进,靳军,支东明.Stratimagic软件在准噶尔盆地地震勘探中的应用[J].新疆石油地质,2002,23(3):245–247.Zhuang Xijin,Jin jun,Zhi Dongming.Application of statimagic software in seismic exploration of Junggar Basin[J].Xinjiang Petroleum Geology,2002,23(3):245–247.
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