摘要
Elastic impedance （EI） inversion has been widely used in industry to estimate kinds of elastic parameters to distinguish lithology or even fluid. However, it is found that conventional three-term elastic impedance formula is unstable even with slight random noise in seismic data, due to the m-conditioned co- efficient matrix of elastic parameters. We presented two-term Fatti elastic impedance inversion method, which is more robust and accurate than conventional three-term elastic impedance inversion. In our method, density is ignored to increase the robustness of inversion matrix. Besides, P-impedance and S-impedance, which are less sensitive to random noise, are inverted instead of Vp and Vs in conventional three-term elastic impedance. To make the inversion more stable, we defined the range of K value as a con- straint. Synthetic tests claim that this method can obtain promising results with low SNR （signal noise ratio） seismic data. With the application of the method in a 2D line data, we achieved λp, μp and Vp/Vs sections, which matched the drilled well perfectly, indicating the potential of the method in reservoir prediction.
Elastic impedance （EI） inversion has been widely used in industry to estimate kinds of elastic parameters to distinguish lithology or even fluid. However, it is found that conventional three-term elastic impedance formula is unstable even with slight random noise in seismic data, due to the m-conditioned co- efficient matrix of elastic parameters. We presented two-term Fatti elastic impedance inversion method, which is more robust and accurate than conventional three-term elastic impedance inversion. In our method, density is ignored to increase the robustness of inversion matrix. Besides, P-impedance and S-impedance, which are less sensitive to random noise, are inverted instead of Vp and Vs in conventional three-term elastic impedance. To make the inversion more stable, we defined the range of K value as a con- straint. Synthetic tests claim that this method can obtain promising results with low SNR （signal noise ratio） seismic data. With the application of the method in a 2D line data, we achieved λp, μp and Vp/Vs sections, which matched the drilled well perfectly, indicating the potential of the method in reservoir prediction.
基金
the sponsorship of the National Natural Science Foundation of China (Nos.41004096 and 41230318) for funding this research
