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Abstract In recent years, many important discoveries have been made in the marine deepwater exploration in the South China Sea, which confirms the abundant natural gas resources in this area. However, the prediction of deepwater reservoirs is very challenging because of the complex depositional system and the low exploration level with sparse wells in deepwater areas. To reduce the exploration risks, we develop an integrated prediction strategy for the deepwater gas reservoirs using the Bayesian adaptive seismic inversion and the frequency-dependent fluid mobility attribute. In the seismic inversion, an automatically adjusted prior stabilizer is derived to balance between the vertical resolution and the inversion stability according to the noise level, and the trace-by-trace recursive inversion process, using the inversion result of the previous adjacent trace as the initial model for the next, is adopted to ensure the lateral continuity. In the gas detection, the fluid mobility attribute is calculated by the high precision matching pursuit algorithm to directly indicate the gas reservoirs, with no need to use the well-log or horizon data. We then combine the stratigraphic seismic inversion result with the gas indication fluid mobility attribute to comprehensively predict both the distribution and thickness of gas reservoirs. Synthetic data tests on a well model and a designed seismic signal verify the performances of the seismic inversion and the matching pursuit algorithm. The real data fluid mobility attribute gas detection results of two borehole-side seismic traces show good consistency with the well log interpretation results. Finally, the feasibility of the proposed integrated prediction method is demonstrated by a deepwater application in the South China Sea. Highlights • Bayesian adaptive seismic inversion has high resolution and lateral continuity. • Matching pursuit-based fluid mobility attribute has accurate gas detection results. • Integrated method can effectively predict the thickness of deepwater gas reservoirs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]