Structure-oriented prestack waveform inversion

Author:

Zhang Jian1ORCID,Zhao Xiaoyan2,Li Jingye3,Chen Xiaohong3ORCID

Affiliation:

1. Southwest Jiaotong University, Faculty of Geosciences and Environmental Engineering, Chengdu 611756, China and Southwest Jiaotong University, MOE Key Laboratory of High-Speed Railway Engineering, Chengdu 610036, China. (corresponding author)

2. Southwest Jiaotong University, Faculty of Geosciences and Environmental Engineering, Chengdu 611756, China.

3. China University of Petroleum-Beijing, The State Key Laboratory of Petroleum Resources and Prospecting, National Engineering Laboratory for Offshore Oil Exploration, Changping, Beijing 102249, China.

Abstract

The prediction of elastic parameters (i.e., P-, S-wave velocity, and density) is one of the key tasks of seismic reservoir characterization. The amplitude-variation-with-offset/angle seismic inversion based on the exact Zoeppritz equation (EZE) or its approximations presupposes a single interface and ignores wave-propagation effects, resulting in low accuracy inversion results. The analytical solution of the 1D wave equation (i.e., the reflectivity method [RM]) can simulate more of the totality of wave-propagation effects, including transmission losses and internal multiples, thus improving the accuracy of the inversion results. However, the RM-based inversion method is sensitive to noise and is usually performed trace-by-trace. When trace-by-trace-based inversion results are combined into a 2D profile, the lateral continuity of the final results is poor, which affects the subsequent interpretation and evaluation. To address these issues, the RM-based structure-oriented prestack waveform inversion (SORM) method is proposed to suppress the effects of data noise and improve the geologic reliability of the inversion results. This method adds an additional structure-oriented constraint term to the objective function, which facilitates the integration of the structural orientation into the inversion algorithm in the form of dips. We carry out the method on a synthetic model as well as on a field data set. A series of numerical tests indicate that the SORM gives significantly more accurate and geologically reliable results compared with inversion based on EZE or trace-by-trace RM.

Funder

National Natural Science Foundation of China

the National Key Research and Development Program of China

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Seismic Inversion Based on Fusion Neural Network for the Joint Estimation of Acoustic Impedance and Porosity;IEEE Transactions on Geoscience and Remote Sensing;2024

2. Simultaneous Physics and Model-Guided Seismic Inversion Based on Deep Learning;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Hybrid-Driven High-Resolution Prestack Seismic Inversion;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023

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