Seismic rock physical modelling for gas hydrate-bearing sediments

There are ambiguities and uncertainties in the recognition of gas hydrate seismic reflections and in quantitative predictions of physical information of natural gas hydrate reservoirs from seismic data. Rock physical modelling is a bridge that transforms the seismic information of geophysical observations into physical information, but traditional rock physics models lack descriptions of reservoir microstructures and pore-filling materials. Considering the mineral compositions and pore microstructures of gas hydrates, we built rock physical models for load-bearing and pore-filling gas hydrate-bearing sediments, describe the mineral compositions, pore connectivity and pore shape using effective media theory, calculated the shear properties of pore-filling gas hydrates using Patchy saturation theory and Generalized Gassmann theory, and then revealed the quantitative relation between the elastic parameters and physical parameters for gas hydrate-bearing sediments. The numerical modelling results have shown that the ratios of P-wave and S-wave velocities decrease with hydrate saturation, the P-wave and S-wave velocities of load-bearing gas hydrate-bearing sediments are more sensitive to hydrate saturation, sensitivity is higher with narrower pores, and the ratios of the P-wave and S-wave velocities of pore-filling gas hydrate-bearing sediments are more sensitive to shear properties of hydrates at higher hydrate saturations. Theoretical analysis and practical application results showed that the rock physical models in this paper can be used to calculate the quantitative relation between macro elastic properties and micro physical properties of gas hydrate-bearing sediments, offer shear velocity information lacking in well logging, determine elastic parameters that have more effective indicating abilities, obtain physical parameters such as hydrate saturation and pore aspect ratios, and provide a theoretical basis and practical guidance for gas hydrate quantitative predictions.