Complete-Q model for poro-viscoelastic media in subsurface sensing: Large-scale simulation with an adaptive DG algorithm

Q Zhan, M Zhuang, Z Zhou, JG Liu… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
IEEE Transactions on Geoscience and Remote Sensing, 2019ieeexplore.ieee.org
In this paper, full mechanisms of dissipation and dispersion in poro-viscoelastic media are
accurately simulated in time domain. Specifically, four Q values are first proposed to depict a
poro-viscoelastic medium: two for the attenuation of the bulk and shear moduli in the solid
skeleton, one for the bulk modulus in the pore fluid, and the other one for the solid-fluid
coupling. By introducing several sets of auxiliary ordinary differential equations, the Q factors
are efficiently incorporated in a high-order discontinuous Galerkin algorithm. Consequently …
In this paper, full mechanisms of dissipation and dispersion in poro-viscoelastic media are accurately simulated in time domain. Specifically, four Q values are first proposed to depict a poro-viscoelastic medium: two for the attenuation of the bulk and shear moduli in the solid skeleton, one for the bulk modulus in the pore fluid, and the other one for the solid-fluid coupling. By introducing several sets of auxiliary ordinary differential equations, the Q factors are efficiently incorporated in a high-order discontinuous Galerkin algorithm. Consequently, in the mathematical sense, the Riemann problem is exactly solved, with the same form as the inviscid poroelastic material counterpart; in the practical sense, our algorithm requires nearly negligible extra time cost, while keeping the governing equations almost unchanged. Parenthetically, an arbitrarily nonconformal-mesh technique, in terms of both h- and p-adaptivity, is implemented to realize the domain decomposition for a flexible algorithm. Furthermore, our algorithm is verified with an analytical solution for the half-space modeling. A validation with an independent numerical solver, and an application to a large-scale realistic complex topography modeling demonstrate the accuracy, efficiency, flexibility, and capability in realistic subsurface sensing.
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