Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5781922 | Marine and Petroleum Geology | 2017 | 37 Pages |
Abstract
In the Shenhu area of the northern South China Sea (SCS), canyon systems and focused fluid flow systems increase the complexity of the gas hydrate distribution in the region. It also induces difficulties in predicting the hydrate reservoir characteristics and quantitatively evaluating reservoir parameters. In this study, several inversion methods have been executed to estimate the velocities of strata and gas hydrate concentrations along a profile in the Shenhu area. The seismic data were inverted to obtain the reflection coefficient of each stratum via a spectral inversion method. Stratigraphic horizons were then delineated by tracking the inverted reflectivities. Based on the results of spectral inversion, a low-frequency velocity field of the strata was constructed for acoustic impedance inversion. Using a new iterative algorithm for acoustic impedance inversion, reflection coefficients were converted into velocities, and the velocity variations of the strata along a 2D seismic line were then obtained. Subsequently, gas hydrate saturations at well SH2 were estimated via the shale-corrected resistivity method, the chloride ion concentration method and three different rock physics models. The results were then compared to determine the optimal rock physics model, and the modified Wood equation (MWE) was found to be appropriate for this area. Finally, the inverted velocities and MWE were used to predict the distribution and concentrations of gas hydrates along the seismic line. The estimated spatial distribution of gas hydrates is consistent with that from sonic logging and resistivity data at well SH2, and with the drilling results. Therefore, this method is applicable in areas with no well data, or with few wells, and provides an effective tool for predicting and evaluating gas hydrates using seismic data.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Economic Geology
Authors
Jie Liu, Jianzhong Zhang, Fei Ma, Ming Wang, Yunbao Sun,