Direct inversion of Young's and Poisson impedances for fluid discrimination

作者：Zong, Z.^*; Yin, X.

来源：Geofluids, 2016, 16(5): 1006-1016.

DOI：10.1111/gfl.12202

• AVO inversion
• Bayesian theory
• fluid discrimination
• the product of Poisson ratio and density
• the product of Young's modulus and density

Seismic inversion with prestack seismic data such as amplitude variation with offsets (AVO) inversion is an important tool in the estimation of elastic parameters for predicting lithology and discriminating fluid in conventional or unconventional hydrocarbon reservoirs. The product of Young's modulus and density (Young's impedance, YI) and the product of Poisson ratio and density (Poisson ratio impedance, PI) show great potential in lithology prediction and fluid discrimination of unconventional resources such as shale gas or oil. The high quality requirements for prestack data in density inversion render the estimation of YI and PI arduous and inaccurate with a conventional prestack inversion approach. In this study, a direct AVO inversion approach is proposed to estimate YI, PI, and density directly from P-wave seismic data. The linearized P-wave reflectivity approximate equation in terms of YI, PI, and density is initially derived. Five models, including four typical AVO classes, are utilized to verify the accuracy of the derived linearized P-wave reflectivity equation in comparison with the exact P-wave reflectivity equation and the frequently used linearized reflectivity approximate equation involving P-and S-wave velocities and density. Parameter sensitivity analysis illustrates that YI and PI can reasonably be estimated from P-wave reflectivity if a decorrelation scheme is utilized in the inversion algorithm. In addition, a pragmatic AVO inversion using a Bayesian scheme is suggested for the direct inversion of YI and PI from prestack seismic data. Synthetic and field data examples demonstrate the feasibility of the proposed inversion approach in the estimation of YI and PI and show the potential of this approach in fluid discrimination.

• 出版日期2016-12
• 单位中国石油大学（北京）