Speaker
Description
Modeling of surface roughness effect is critical to accurately evaluate pore structures from measurable (such as electrical and electromagnetic) signals. Previous works successfully characterized the 3D pore surface roughness, but only modeled its effect on the surface relaxation of nuclear magnetic resonance (NMR) for individual pores. It becomes unambiguously complicated extending to digital rocks, as the surface relaxation in each segmented pore bodies must be corrected individually. This work proposes a practical way to upscale the modeling of the surface roughness effect from pore scale to core scale.
This work aims to establish a physics-consistent mapping between the surface roughness and relaxation correction factor at the core scale. The proposed workflow includes three main steps. The first step is to segment the connected pore space into a plurality of disconnected pore geometries. We leverage spherical harmonics to model pore surface roughness and parameterize the magnitude of surface roughness into a dimensionless number. Then the roughness correction factors are calculated by the high-fidelity random walk simulation. In the last step, we upscale the surface roughness at the core scale by representatively sampling pore surface roughness in terms of the characteristics of pore shape and geometry, and establish a correlation between surface roughness and roughness correction factor for different rock types.
The effectiveness of the proposed method is verified by comparing the pore size distributions interpreted from NMR T2 relaxation time with pore size distributions extracted from pore network models. With properly correcting the surface roughness effect, the NMR-based pore size distributions shift to larger pore sizes, with the peak position of the pore size distribution consistent with the result of pore network modeling. Unlike the benchmarking scheme that calculates the roughness correction factor for each segmented pore and populates all the correction factors back to the digital rock, the established data-driven model provides a proper roughness correction factor for each pore type, making the resultant T2 curve agree with the benchmarking scheme very well. It is worth noting that the pore separation algorithm plays a critical role in the success of proposed method, since it is extremely challenging to directly model the surface roughness of the whole interconnected pore space. The segmented pore bodies have to be simple enough but also not over-segmented.
| Country | Saudi Arabia |
|---|---|
| Acceptance of the Terms & Conditions | Click here to agree |
