Time-lapse full waveform inversion based on curvelet transform: Case study of CO2 storage monitoring. (September 2021) - Li, Dong; Peng, Suping; Huang, Xingguo; Guo, Yinling; Lu, Yongxu; Cui, Xiaoqin
Highlights: A multi-scale full waveform inversion method based on curvelet transform is proposed. According to the high discreteness of the curvelet transform, the shot gather data is reconstructed into multiple scales. The sequential inversion strategy is used to simulate the time-lapsed difference of the reservoir before and after CO2 injection. This high-precision reservoir elastic parameter inversion method is used for CO2 storage monitoring. Abstract: Full waveform inversion (FWI) has high accuracy in predicting reservoir elastic parameters. Prediction of changes in reservoir elastic parameters is an effective method for CO2 storage monitoring. Therefore, FWI is of great significance in CO2 storage monitoring, however, it relies on low-frequency information and data quality and its use incurs substantial computational costs. To alleviate this problem, a multi-scale time-lapse inversion method based on the curvelet transform was proposed. Using the high sparsity of the curvelet transform, the original shot gather data can be separated into multiple scales. A multi-scale inversion process based on the curvelet transform was introduced, and a coarse to fine scale inversion was conducted. According to the multi-scale inversion strategy, the P- and S-wave velocities and densities of the reservoirs in multiple periods were obtained. The test results of Marmousi II and the manual model show that the inversion method based on the curvelet transform exhibits high accuracy in predicting reservoir elastic parameters and can clearly determine the local changes in the reservoir. The calculation time shows that the multi-scale inversion based on the curvelet transform does not reduce the calculation efficiency. In summary, the time-lapse full waveform inversion method based on the curvelet transform is highly accurate, and is therefore promisingly effective for CO2 storage monitoring.