Gas prediction using low-frequency components of variable-depth streamer seismic data applied to the deepwater area of the South China Sea

Abstract

The deepwater area of the South China Sea has become a contentious aspect of marine oil and gas exploration in China in recent years due to its abundance of gas reservoir resources. However, the significant burial depth, rugged seabed, complex geological structure and notch effect resulting from the conventional constant-depth streamer (CDS) acquisition method give rise to missing low- and high-frequency components of deep signals. In addition, CDS seismic data present a low signal-to-noise ratio and resolution, thereby complicating gas prediction. Nevertheless, the variable-depth streamer (VDS) acquisition method can not only protect low- and high-frequency data and broaden the frequency band of seismic data, but it is also highly reliable and presents significant amplitude-preserving capacities. In consideration of these outstanding features, we propose combining low-frequency components of VDS data and fluid mobility attributes to predict gas reservoir distributions. Using a broadband wavelet and a narrowband wavelet as source wavelets to simulate VDS and CDS data of a 2D gas-bearing geologic model, we adopt a matching pursuit algorithm based on the Wigner-Ville distribution to calculate individually fluid mobility attributes corresponding to these two synthetic datasets of 1–10 Hz. The results of our studies based on synthetic and real data indicate that abundant low frequency components of VDS data can more accurately and objectively predict gas reservoir distributions while preventing the generation of multiple solutions for identifying gas reservoirs using low-frequency signals of CDS data. As a result, VDS seismic data can serve as a more precise and reasonable basis for gas exploration and development in the deepwater area of the South China Sea.