1. Non-Repetitive Time-Shifted Seismic Monitoring Study Based on Ocean Bottom Cable and Towed Streamer Data
- Author
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Fengying Chen, Xiangchun Wang, Wei Liu, Yibin Li, and Zhendong Liu
- Subjects
non-repetitive time-shifted seismic research ,time-shifted monitoring ,data reconstruction ,amplitude attributes ,Naval architecture. Shipbuilding. Marine engineering ,VM1-989 ,Oceanography ,GC1-1581 - Abstract
Time-shifted seismic research plays an important role in monitoring changes in the gas-water interface uplift, the weakening of amplitude attributes, and gas distribution due to mining. When time-shifted seismic research involves non-repeatable data with significant differences between data sets due to variations in seismic data acquisition parameters and seismic geometries, it necessitates consistent processing before time-shifted monitoring comparisons. In this paper, a study of time-shifted seismic monitoring using two non-repetitive data sets based on the ocean bottom cable (OBC) and towed streamer data is presented. First, amplitude, frequency, wavelet, and time difference are processed to achieve consistency for time-shifted comparisons. Secondly, three modes of seismic geometry normalization are compared to optimize the appropriate offset, azimuth, and signal-to-noise ratio (SNR). Finally, after eliminating the fault surface wave, the maximum trough amplitude attribute is extracted for the same position in the two data sets to analyze time-shifted differences under the three modes using the ratio method and difference method. The conclusions show the following: the OBC and towed streamer data can achieve consistency in terms of amplitude, frequency, wavelet, azimuth, SNR, and time difference; the data reconstruction method outperforms other methods in normalizing offset, azimuth, and SNR; and the time-shifted comparison method of the amplitude attribute ratio method proves more effective than the difference method. This study offers a reliable foundation for future time-shifted seismic research with non-repetitive data to monitor changes in subsurface oil and gas. It also provides a methodological basis for carbon capture and storage (CCS) monitoring technology.
- Published
- 2024
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