Integrated prediction of deepwater gas reservoirs using Bayesian seismic inversion and fluid mobility attribute in the South China Sea

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.