Image processing of high-resolution,
3D images to create digital representations of pore micro-structures for
Image-Based Rock Physics simulations remains a highly subjective enterprise
despite the seemly precision associated with improving imaging resolutions and
intensive parallel computations. The decisions on how to identify pore space,
both macro- and micro-pores, and various mineral components remains very much
dependent upon user choices and biases. Treatment of sub-resolution features,
i.e., mineral facies with micro-porosity that are below the imaging resolution,
is critical in tight reservoirs, but rarely included systematically in the
analysis. A set of shally-sand samples with a significant amount of authigenic
chlorite/smectite that lines the larger pores was tested to identify
uncertainty quantification (UQ) requirements associated with image-processing
steps-- segmentation in particular. Two segmentation strategies, conventional
thresholding based and artificial intelligence (AI) based, are employed with
different UQ parameter space. Relative proportions of macro- and micro-pores in
these samples, and their spatial distributions with regard to pore-lining clay
mineral, are iteratively studied over the defined UQ parameter space, and
cross-validated by independent NMR measurements. The pore micro-structure
extracted from these different iterations were the basis of simulations of
basic petrophysical properties. Upon cross-validation with measured core
properties, a UQ framework is proposed to assess the differences between the
different measurements from three angles: sampling, numerical and physical.
For more information, request a copy of the SCA 2018 Paper.