Speaker
Description
Porosity maps are essential tools for understanding the spatial distribution of pore space in rocks and serve as a foundation for numerical simulations that model fluid flow in porous media. Moreover, the spatial heterogeneity of porosity enables the identification of regions with greater or lesser storage and flow capacity. In this study, porosity maps were generated from X-ray micro-computed tomography (micro-CT) images. Conventionally, porosity maps are obtained by scanning the same sample under two conditions: dry and fully saturated with a saline solution such as sodium iodide (NaI). The difference in intensity between these two scans allows for voxel-wise estimation of porosity based on the variation in fluid content. The resulting image is then normalized so that in fully porous regions (saturated with fluid in the wet scan) become 1 and fully non-porous regions become 0.
To ensure image quality and reliability, reference standards shaped as hole saw inserts—composed of materials such as aluminum, quartz, and Teflon—were included during micro-CT acquisition. These standards act as beam-hardening correction references, serving as calibration filters for the tomographic images. Nevertheless, natural fluctuations in the X-ray beam generated by the micro-CT scanner can lead to variation in attenuation values for the same material across different experiments. The introduction of reference standards allows for the correction—or at least mitigation—of this issue by ensuring that the reference materials yield consistent attenuation values across all scans. This is achieved through an initial segmentation step to automatically detect the regions corresponding to the standards. Given the material uniformity, the mode of the intensity values within each standard is extracted. A reference scan is then selected, and the attenuation values of the standards in all other scans are interpolated to match those of the reference, thereby bringing all datasets to a common intensity scale. Once this calibration is complete, porosity estimation from dry samples becomes feasible. This is accomplished by statistically mapping the attenuation values of the dry sample to porosity values of the porosity mapsusing the cumulative distribution functions (CDFs) of the dry image and the corresponding porosity map since the samples are paired and represent the exact same material just in different conditions.
The mapping between CDFs defines a transformation function that can then be applied to dry-only scans to generate porosity maps. This approach was validated using a set of twelve samples that underwent the full experimental workflow for porosity map generation. The resulting mapping function was then applied to an independent set of 343 dry samples from PETROBRAS. Porosity maps were generated for each dry sample, and the bulk porosity was computed and compared with laboratory-measured porosity values.
| Country | Brazil |
|---|---|
| Acceptance of the Terms & Conditions | Click here to agree |
