ZoneID® and PoreHD® Services
Reservoir Rock Properties from SEM and FIB-SEM Imaging
Understand the pore system and quantify the relative producibility of different rock textures with Ingrain’s state-of-the-art imaging and interpretation in 2D and 3D. Our proprietary offering provides unmatched image quality and a quantitative understanding of representative volume fractions and pore morphology. We deliver key insight about storage potential and help assess the flow of hydrocarbons in your reservoir.
Volume fractions of organic matter (OM) total porosity, intergranular porosity (IG) and organic matter porosity (PAOM) are computed for each 2D SEM image. This is performed at the resolution required to characterize your rock. These porosity types enable the quantification of the fraction of original solid organic matter that has been preserved and converted to porosity. This is abbreviated "Apparent Transformation Ratio" (ATR) = PAOM/(PAOM + Solid OM).
Additionally, we obtain pore type, size and shape on the same sample. Pore morphology information can be used to estimate specific surface area and total pore volume to assess for permeability. With Ingrain’s ZoneID® service, you can rapidly and cost-effectively predict fluid-flow transport properties in relation to the different porosity types present in your reservoir.
Investigate further with Ingrain’s PoreHD® characterization service in three dimensions. The PoreHD® service improves the understanding of the pore and matrix, providing a visual and quantitative analysis of volume fractions such as porosity. This includes total, connected and isolated, porosity associated with organic matter (PAOM), permeability – vertical and horizontal, and pore morphology data sets.
This data will allow you to better understand the relationships between porosity and permeability for each of the primary producing facies. This is an important component for reservoir characterization.
Ingrain applies FabricsML® methodology to ensure representativeness of your volumetrics. This tool propagates digital rock properties from a small subsample to a larger sample via fabric recognition and machine learning.