Reservoir characterization and hydrocarbon accumulation in late Cenozoic lacustrine mixed carbonate-siliciclastic fine-grained deposits of the northwestern Qaidam basin, NW China

2018 ◽  
Vol 98 ◽  
pp. 675-686 ◽  
Author(s):  
Wei Zhang ◽  
Xing Jian ◽  
Ling Fu ◽  
Fan Feng ◽  
Ping Guan
2013 ◽  
Vol 40 (2) ◽  
pp. 170-182 ◽  
Author(s):  
Xiaorong LUO ◽  
Ying SUN ◽  
Liqun WANG ◽  
Ancheng XIAO ◽  
Lixie MA ◽  
...  

2022 ◽  
Vol 41 (1) ◽  
pp. 47-53
Author(s):  
Zhiwen Deng ◽  
Rui Zhang ◽  
Liang Gou ◽  
Shaohua Zhang ◽  
Yuanyuan Yue ◽  
...  

The formation containing shallow gas clouds poses a major challenge for conventional P-wave seismic surveys in the Sanhu area, Qaidam Basin, west China, as it dramatically attenuates seismic P-waves, resulting in high uncertainty in the subsurface structure and complexity in reservoir characterization. To address this issue, we proposed a workflow of direct shear-wave seismic (S-S) surveys. This is because the shear wave is not significantly affected by the pore fluid. Our workflow includes acquisition, processing, and interpretation in calibration with conventional P-wave seismic data to obtain improved subsurface structure images and reservoir characterization. To procure a good S-wave seismic image, several key techniques were applied: (1) a newly developed S-wave vibrator, one of the most powerful such vibrators in the world, was used to send a strong S-wave into the subsurface; (2) the acquired 9C S-S data sets initially were rotated into SH-SH and SV-SV components and subsequently were rotated into fast and slow S-wave components; and (3) a surface-wave inversion technique was applied to obtain the near-surface shear-wave velocity, used for static correction. As expected, the S-wave data were not affected by the gas clouds. This allowed us to map the subsurface structures with stronger confidence than with the P-wave data. Such S-wave data materialize into similar frequency spectra as P-wave data with a better signal-to-noise ratio. Seismic attributes were also applied to the S-wave data sets. This resulted in clearly visible geologic features that were invisible in the P-wave data.


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