We characterize the complex pore architecture of four diagenetically altered sandstone samples using micro-CT imaging.By quantifying the pore size and pore connection distributions,we establish that the mean coordinat...We characterize the complex pore architecture of four diagenetically altered sandstone samples using micro-CT imaging.By quantifying the pore size and pore connection distributions,we establish that the mean coordination number follows a power-law function of pore radius.Critically,we derive empirical scaling relations between 2D and 3D pore size distributions,as well as 2D and 3D coordination number distributions,both as a function of pore size.These novel relations allow for the 3D pore size and pore connectivity to be accurately characterized from high-resolution 2D imaging(e.g.,SEM),even when the resolution or field-of-view limitations preclude detailed 3D micro-CT analysis.A new stochastic algorithm is presented in this work to generate a 3D pore network model that honors the derived connectivity function.This algorithm was validated by generating stochastic models equivalent to the four sandstone samples and successfully comparing the calculated single-phase and relative permeability results against those obtained from pore networks directly extracted from the 3D micro-CT images.This methodology offers a computationally efficient and scalable tool for predicting the macroscopic transport properties of complex reservoir rocks based on readily available 2D high rock resolution images.展开更多
文摘We characterize the complex pore architecture of four diagenetically altered sandstone samples using micro-CT imaging.By quantifying the pore size and pore connection distributions,we establish that the mean coordination number follows a power-law function of pore radius.Critically,we derive empirical scaling relations between 2D and 3D pore size distributions,as well as 2D and 3D coordination number distributions,both as a function of pore size.These novel relations allow for the 3D pore size and pore connectivity to be accurately characterized from high-resolution 2D imaging(e.g.,SEM),even when the resolution or field-of-view limitations preclude detailed 3D micro-CT analysis.A new stochastic algorithm is presented in this work to generate a 3D pore network model that honors the derived connectivity function.This algorithm was validated by generating stochastic models equivalent to the four sandstone samples and successfully comparing the calculated single-phase and relative permeability results against those obtained from pore networks directly extracted from the 3D micro-CT images.This methodology offers a computationally efficient and scalable tool for predicting the macroscopic transport properties of complex reservoir rocks based on readily available 2D high rock resolution images.
