Three-parameter prestack nonlinear inversion constrained by gradient structure similarity

Estimating the elastic parameters from prestack inversion is of great importance for reservoir characterization. Conven-tional three-parameter prestack inversion methods rely heavily on well logs, and it is difficult to obtain reliable inversion re-sults in situations with limited numbers of wells. Alternatively, we have developed a joint inversion strategy, integrating the advantages of post-and prestack inversion, to deal with the situation. First, due to the high signal-to-noise ratio of post -stack seismic data, the high-precision acoustic impedance (AI) inversion is conducted. Second, the exact Zoeppritz equa-tion is used to establish the objective function of the prestack inversion. To better constrain the elastic parameters (P-and S -wave velocities and density), a new similarity measurement criterion, the gradient structure similarity (GSS), is defined to describe the structural similarity between the prestack inver-sion results and the inverted AI from the poststack inversion. Third, the LM optimization algorithm is used to solve the non-linear objective function. Through the model test, we verify the effectiveness of our GSS regularization scheme. Some synthetic and field examples find that our method can provide more stable and accurate inverted results relative to the con-ventional prestack inversion methods.

Automated summary

Estimating elastic parameters is crucial for reservoir characterization. Traditional prestack inversion methods heavily rely on well logs, making it hard to obtain reliable results with limited well data. To address this, we propose a joint inversion strategy that combines the advantages of post-stack and prestack inversion.