摘要
三维三分量(3D3C)VSP属于地下地震勘探方法,三维三分量VSP地震资料含有深度和丰富的地层岩性信息,有地面地震不可比拟的优势。为了发挥和应用三维三分量VSP资料的优势,提高油气勘探的成功率,需要反演出准确的岩性及弹性参数,用于精确预测储层、识别流体性质。以各向异性介质(VTI)弹性波传播理论为基础,研究多波精确振幅特征方程(AVA),并在此基础上建立了三维各向异性介质VSP多波联合叠前AVA反演岩性参数理论和方法。该方法利用对3D3CVSP资料处理的NMO道集,直接反演地层纵波速度、横波速度、密度及2个各向异性系数,在此基础上可进一步计算得到地层的弹性参数,这些参数对岩性及流体识别有重要作用。这套纵横波联合叠前反演方法适应直井及斜井3D3C VSP资料,并经实际斜井3D3CVSP资料处理,得到了与实际储层吻合的反演结果,证明了该方法的正确性和有效性。
The three-dimensional and three component（3D 3C）VSP belong to the underground seismic exploration method,and the VSP data contain abundant depth and lithology information.Therefore,three-dimensional and three component（3D 3C ）VSP have incomparable advantage over ground seismic.Accurate inversion of lithology and elastic parameters is needed by three-dimensional and three component VSP data in the subsequent application to precise prediction of reservoir and recognition of fluid property,so as to improve the success rate of oil and gas exploration.Exact multiwave amplitude-versus-angle（AVA）equations are studied based on the theory of elastic wave propagation in anisotropic media（VTI）,and the theory and method for three-dimensional（3D）vertical seismic profiling（VSP）of multiwave joint pre-stack AVA lithologic parameter inversion for anisotropic media are established.This method directly inverts the P-wave（compressional wave）velocity,S-wave（shear wave）velocity,density,and two anisotropy coefficients of strata by using normal move out gathers from 3D three-component（3C）VSP data processing.Based on these inversions,the elastic parameters of the strata,which are important in identification of lithology and fluids,can be further calculated.This method of P-wave and S-wave joint pre-stack inversion is suitable for 3D 3C VSP data of vertical and inclined drilling wells.This method is proved to be correct and effective in the practical validation of inclined drilling well,which is predicted by actual processing of 3D 3C VSP data.
出处
《成都理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第5期513-520,共8页
Journal of Chengdu University of Technology: Science & Technology Edition
基金
国家科技重大专项(2008ZX05024-001)
