摘要
页岩中的有机质干酪根广泛发育大量纳米级别的孔隙,这些孔隙具有极大的表面-体积比,孔隙表面会产生明显的表面效应,从而对页岩整体弹性性质产生影响.在对页岩进行储层预测时,考虑纳米孔隙的影响是十分必要的.现有的AVO技术以经典弹性理论为基础,通常可以预测页岩储层的纵横波速度、弹性模量和密度等,但往往忽略了纳米孔隙的表面效应.结合纳米孔隙弹性理论和AVO技术,将表征纳米孔表面效应的参数(表面弹性模量、纳米孔径)耦合到反射系数近似公式中,提出了一种新的AVO参数化表征方法,直接体现了纳米孔隙相关参数对反射系数的响应特征.新方法中包含3个待反演参数:基质剪切模量、纳米孔隙相关参数和密度.基于平滑背景约束的贝叶斯反演策略,利用测井的模型试验和实际地震数据验证了所提方法的可行性和适用性,实现了对页岩纳米孔隙相关属性的定量预测.通过定量关系转换,创建了一种用以描述页岩含气能力的指示因子.本文提出的方法为预测页岩气储层弹性性质提供了一种新的思路.
A large number of nanopores are widely developed in organic kerogen in shale.These pores own a very large surface-volume ratio,and the pore surface produce obvious surface effects,which affect the overall elastic properties of shale.It is necessary to consider the effect of nanopores in shale reservoir prediction.Existing AVO techniques,based on classical elastic theory,can usually predict the P-wave velocity and S-wave velocity,elastic modulus and density of shale reservoirs,but often ignore the surface effects of nanopores.By combining the nanoporous elastic theory and AVO technology,the surface elastic modulus and nanopores radius are coupled to the approximate formula of reflection coefficient,and a new parameterized AVO characterization method is proposed accordingly,which directly reflects the response characteristics of nanopores related parameters to reflection coefficient.The new method contains three parameters to be inverted:matrix shear modulus,nanopores related parameter and density.Based on the Bayesian inversion strategy with smooth background constraint,the feasibility and applicability of the proposed method are verified by logging model test and actual seismic data,and the quantitative prediction of shale nanopores related properties is realized.Through the transformation of quantitative relationship,an indicator to describe the gas bearing capacity of shale is built.The method proposed in this paper provides a new idea for predicting shale gas reservoir elastic properties.
作者
印林杰
印兴耀
李坤
YIN LinJie;YIN XingYao;LI Kun(School of Geoscience,China University of Petroleum,Qingdao 266580,China;Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266580,China;Shandong Provincial Key Laboratory of Deep Oil and Gas,Qingdao 266580,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2023年第9期3869-3881,共13页
Chinese Journal of Geophysics
基金
国家自然科学基金(41974119,42030103)联合资助。
