井、震多尺度信息融合预测老油田浅层岩性气藏
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摘要
老油田浅层岩性气藏岩性分布复杂,利用地震属性分析、含气砂岩地震正演模拟及基于概率神经网络(PNN)的声波时差参数预测等方法对气藏分布进行描述,是寻找该类气藏的有效手段,其中建立符合研究区地质特征的地质模型,开展地震正演研究,总结含气砂体的地震反射特征,是利用地震反射资料进行含气目标检验的前提。在属性优选的基础上,利用PNN神经网络算法将多种地震属性与声波时差测井信息相结合,对三维空间的声波时差参数分布特征进行预测,一方面避免了单一地震属性信息的片面性,另一方面实现了气藏敏感测井参数的合理延伸,是一种快速有效的气藏检测方法。将井、震多尺度信息融合预测浅层岩性气藏方法应用于吉林红岗油气田,地震正演结果显示,气藏顶部为较强的波谷反射,底部为强波峰反射特征,地震属性异常区的特征在地震反射剖面上与正演的含气砂岩反射特征相近,从而查明了HI3气藏的主控因素,并获得高产气井。
The lithology distribution for shallow gas reservoir in mature oilfield is quite complex,using seismic attribute analysis,gas-bearing sandstone forward modeling and sonic parameter prediction method which is based on Probability Neural Networks(PNN) to predict the distribution of the shallow gas reservoir are the effective tools for exploration of this kind of reservoir.Building geological model which is consistent with regional geology trends and applying the model in seismic forward modeling studies,summarizing the seismic reflection characteristics of the gas reservoirs,are the precondition for reservoir prediction in surface seismic exploration.Based on optimized attributes,PNN is used to integrate multi seismic attributes with well logging sonic data,then the distribution characteristics of the sonic parameters in 3d space can be predicted.On the one hand the workflow above avoid the information sidedness caused when only using single seismic attribute,on the other hand it realizes the reasonable extension of sonic parameters in the zone of interests over the 3d space,therefore the workflow provides a fast and effective tool for gas reservoir prediction.The workflow was applied in Honggang oilfield,Jilin,the seismic forward modeling results show that strong trough reflection from the top of the gas reservoir and strong peak reflection from the bottom of the reservoir can be seen,the anomalies of seismic attributes on seismic sections and the reflection characteristics from forward modeling gas-bearing sandstone are similar,thus major control factor for HI3 gas reservoir was identified,and the highly productive gas wells were the awards for the integrated studies.
The lithology distribution for shallow gas reservoir in mature oilfield is quite complex,using seismic attribute analysis,gas-bearing sandstone forward modeling and sonic parameter prediction method which is based on Probability Neural Networks(PNN) to predict the distribution of the shallow gas reservoir are the effective tools for exploration of this kind of reservoir.Building geological model which is consistent with regional geology trends and applying the model in seismic forward modeling studies,summarizing the seismic reflection characteristics of the gas reservoirs,are the precondition for reservoir prediction in surface seismic exploration.Based on optimized attributes,PNN is used to integrate multi seismic attributes with well logging sonic data,then the distribution characteristics of the sonic parameters in 3d space can be predicted.On the one hand the workflow above avoid the information sidedness caused when only using single seismic attribute,on the other hand it realizes the reasonable extension of sonic parameters in the zone of interests over the 3d space,therefore the workflow provides a fast and effective tool for gas reservoir prediction.The workflow was applied in Honggang oilfield,Jilin,the seismic forward modeling results show that strong trough reflection from the top of the gas reservoir and strong peak reflection from the bottom of the reservoir can be seen,the anomalies of seismic attributes on seismic sections and the reflection characteristics from forward modeling gas-bearing sandstone are similar,thus major control factor for HI3 gas reservoir was identified,and the highly productive gas wells were the awards for the integrated studies.
引文
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[2]Law B E and Curtis J B.Introduction to unconven-tional petroleum systems.AAPG Bulletin,2002,86(11):1851～1852
[3]王兴谋,韩文功,李红梅等.浅层岩性气藏地震检测的陷阱分析.石油大学学报(自然科学版),2003,27(1):19～22
[4]邹才能,陶士振,谷志东.陆相坳陷盆地层序地层格架下岩性地层圈闭/油藏类型与分布规律——以松辽盆地白垩系泉头组—嫩江组为例.地质科学,2006,41(4):711～719
[5]赵波,张顺,林春明等.松辽盆地坳陷期湖盆层序地层研究.地层学杂志,2008,32(2):161
[6]王永刚.地震资料综合解释方法.山东东营:中国石油大学出版社,2007
[7]Hidalgo R,Haryono S and Schneider Met al.3-D seismic interpretation of thin gas sandstone reservoirs using multiattribute analysis on poststack and AVO data,offshore SE Sumatra.The Leading Edge,2001,20(4):364～370
[8]印兴耀,周静毅.地震属性优化方法综述.石油地球物理勘探,2005,40(4):482～489
[9]宋维琪,刘江华,王小马等.预测油气的地震属性优化组合、灰关联分析技术.石油勘探与开发,2002,29(5):34～36
[10]徐旺林,庞雄奇,吕淑英等.动态概率神经网络及油气概率分布预测.石油地球物理勘探,2005,40(1):65～70
[11]Berge T B and Aminzadeh F.Seismic inversion suc-cessfully predicts reservoir,porosity,and gas content in Ibhubesi Field,Orange Basin,South Africa.The Leading Edge,2002,21(4):338～348
[2]Law B E and Curtis J B.Introduction to unconven-tional petroleum systems.AAPG Bulletin,2002,86(11):1851～1852
[3]王兴谋,韩文功,李红梅等.浅层岩性气藏地震检测的陷阱分析.石油大学学报(自然科学版),2003,27(1):19～22
[4]邹才能,陶士振,谷志东.陆相坳陷盆地层序地层格架下岩性地层圈闭/油藏类型与分布规律——以松辽盆地白垩系泉头组—嫩江组为例.地质科学,2006,41(4):711～719
[5]赵波,张顺,林春明等.松辽盆地坳陷期湖盆层序地层研究.地层学杂志,2008,32(2):161
[6]王永刚.地震资料综合解释方法.山东东营:中国石油大学出版社,2007
[7]Hidalgo R,Haryono S and Schneider Met al.3-D seismic interpretation of thin gas sandstone reservoirs using multiattribute analysis on poststack and AVO data,offshore SE Sumatra.The Leading Edge,2001,20(4):364～370
[8]印兴耀,周静毅.地震属性优化方法综述.石油地球物理勘探,2005,40(4):482～489
[9]宋维琪,刘江华,王小马等.预测油气的地震属性优化组合、灰关联分析技术.石油勘探与开发,2002,29(5):34～36
[10]徐旺林,庞雄奇,吕淑英等.动态概率神经网络及油气概率分布预测.石油地球物理勘探,2005,40(1):65～70
[11]Berge T B and Aminzadeh F.Seismic inversion suc-cessfully predicts reservoir,porosity,and gas content in Ibhubesi Field,Orange Basin,South Africa.The Leading Edge,2002,21(4):338～348
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