塔中奥陶系碳酸盐岩缝洞型储层贝叶斯叠前反演预测研究
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摘要
为了对塔中地区顺托果勒南区块SN1井区碳酸盐岩缝洞型储层进行精细预测,开展了基于三维叠前地震数据的贝叶斯反演技术应用研究,重点论述了测井曲线重构、岩石物理分析、敏感弹性参数验证、叠前反演角道集优选等关键技术环节。利用神经网络技术建立测井响应与地层参数之间的关系模型,重构出垮塌井段的纵、横波速度及密度测井曲线;通过建立缝洞型储层的地震地质模型,利用正、反演技术同时验证了贝叶斯叠前反演在SN1井区的适用性及敏感弹性参数识别储层的有效性。最后将叠后曲率属性与叠前反演的敏感弹性参数属性进行融合,对碳酸盐岩缝洞型储层进行精细刻画,预测出SN1井区两条相交断裂带的交会区域为最有利勘探区带。
In order to precisely predict the fractured-vuggy carbonate reservoir characteristics of SN1 well area in Tazhong area,the application research of Bayesian inversion technique based on 3Dprestack seismic data is conducted.This paper is focusing on well logs reconstruction,rock physical analysis,verification of sensitive elastic parameters and angle-gathers optimization of prestack inversion.By using neural network technique,we established the relation models between log response and reservoir parameters,with which we reconstructed the well logs of S-wave and P-wave velocities and density.Making use of forward and inversion techniques for seismic-geological models of fractured-vuggy carbonate reservoir,we proved the capability of prestack Bayesian inversion and the effectiveness of reservoir recognition by sensitive elastic parameters in SN1 well area.At the end of this paper,by the integration of poststack curvature attribute and sensitive elastic parameters from the prestack Bayesian inversion we carried out fine characterization of fracture-vuggy reservoirs of SN1 well area and predicted that the intersection area of the two fault zones is the most favorable exploration area.
In order to precisely predict the fractured-vuggy carbonate reservoir characteristics of SN1 well area in Tazhong area,the application research of Bayesian inversion technique based on 3Dprestack seismic data is conducted.This paper is focusing on well logs reconstruction,rock physical analysis,verification of sensitive elastic parameters and angle-gathers optimization of prestack inversion.By using neural network technique,we established the relation models between log response and reservoir parameters,with which we reconstructed the well logs of S-wave and P-wave velocities and density.Making use of forward and inversion techniques for seismic-geological models of fractured-vuggy carbonate reservoir,we proved the capability of prestack Bayesian inversion and the effectiveness of reservoir recognition by sensitive elastic parameters in SN1 well area.At the end of this paper,by the integration of poststack curvature attribute and sensitive elastic parameters from the prestack Bayesian inversion we carried out fine characterization of fracture-vuggy reservoirs of SN1 well area and predicted that the intersection area of the two fault zones is the most favorable exploration area.
引文
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[13]杨志力,周路,彭文利,等.BP神经网络技术在声波测井曲线重构中的运用[J].西南石油大学学报,2008,30(1):63-66Yang Z L,Zhou L.Application of BP neural network technology in sonic log data rebuilding[J].Journal of Southwest Petroleum University(Sience&Technology Edition),2008,30(1):63-66
[14]耿福兰.南堡2号潜山碳酸盐岩储层叠前和叠后裂缝综合预测[J].石油物探,2013,52(1):89-96Geng F L.Carbonate reservoir fracture prediction by using poststack and pre-stack seismic techniques in Nanpu2buried-hill[J].Geophysical Prospecting for Petroleum,2013,52(1):89-96
[2]Buland A,More H.Bayesian linearized AVO inversion[J].Geophysics,2003,68(1):185-198
[3]Buland A,More H.Bayesian wavelet estimation from seismic and well data[J].Geophysics,2003,68(6):2000-2009
[4]Bachrach R.Joint estimation of porosity and saturation using stochastic rock-physics modeling[J].Geophysics,2006,71(5):53-63
[5]Larsen A L,Ulvmoen M,Omre H.Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model[J].Geophysics,2006,71(5):69-78
[6]Ulvmoen M,Omre H.Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations:part 1—methodology[J].Geophysics,2010,75(2):21-35
[7]Ulvmoen M,Omre H.Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations:part 2-real case study[J].Geophysics,2010,75(2):73-82
[8]杨培杰,印兴耀.非线性二次规划贝叶斯叠前反演[J].地球物理学报,2008,51(6):1876-1882Yang P J,Yin X Y.Non-linear quadratic programming Bayesian prestack inversion[J].China Journal of Geophysics,2008,51(6):1876-1882
[9]张丰麒,魏福吉,王彦春,等.基于精确Zoeppritz方程三变量柯西分布先验约束的广义线性AVO[J].地球物理学报,2013,56(6):2098-2115Zhang F Q,Wei F J,Wang Y C,et al.Generalized linear AVO inversion with thepriori constraint of trivariate Cauchy distribution based on Zoeppritz equation[J].Chinese Journal of Geophysics,2013,56(6):2098-2115
[10]印兴耀,周琪超.基于t分布为先验约束的叠前AVO反演[J].石油物探,2014,53(1):84-92Yin X Y,Zhou Q C.AVO inversion with t-distribution as priori constrain[J].Geophysical Prospecting for Petroleum,2014,53(1):84-92
[11]陈建江.AVO三参数反演方法研究[D].青岛:中国石油大学,2007Chen J J.Study of three-term AVO inversion method[D].Qingdao:China University of Petroleum,2007
[12]王玉梅.叠前地震反演精度影响因素[J].油气地质与采收率,2013,20(1):55-58Wang Y M.Study of influential factors on pre-stack seismic inversion accuracy[J].Petroleum Geology and Recovery Efficiency,2013,20(1):55-58
[13]杨志力,周路,彭文利,等.BP神经网络技术在声波测井曲线重构中的运用[J].西南石油大学学报,2008,30(1):63-66Yang Z L,Zhou L.Application of BP neural network technology in sonic log data rebuilding[J].Journal of Southwest Petroleum University(Sience&Technology Edition),2008,30(1):63-66
[14]耿福兰.南堡2号潜山碳酸盐岩储层叠前和叠后裂缝综合预测[J].石油物探,2013,52(1):89-96Geng F L.Carbonate reservoir fracture prediction by using poststack and pre-stack seismic techniques in Nanpu2buried-hill[J].Geophysical Prospecting for Petroleum,2013,52(1):89-96
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