Speaker
Description
Dual-energy computed tomography (DECT), a non-destructive characterization method of geological samples, has been used for estimating the effective atomic number ($Z_{eff}$), and the density (ρ) of the components of a sample. These estimates require calibration using three materials with known intensities as well as air – typically surrounding the sample – within the scan range, resulting in four reference points at two different X-ray energies. This type of setup is not standard practice, but we argue that it should be.
CT data always contains noise. Incorporating a Monte Carlo (MC) approach into this method quantifies the uncertainty caused by the noisy nature of CT scans, and allows for finding a solution where noise would prevent finding an exact solution. A study by Victor et al. (2017) showed that this method can be used in carbonate samples to improve the estimation of petrophysical properties; however, its application remains untested in clay-bearing sandstones and other rock types. In this study, we validate the application of MC-based inversion of $Z_{eff}$ and ρ in clay-rich sandstones and assess its effectiveness. We focus in particular on improving the saturation estimates, which are often unreliable for clay-rich materials, as well as distinguishing clay types.
| References | Victor, R. A., Prodanović, M., & Torres-Verdín, C. (2017). Monte Carlo Approach for Estimating Density and Atomic Number From Dual-Energy Computed Tomography Images of Carbonate Rocks. Journal of Geophysical Research: Solid Earth, 122(12), 9804–9824. https://doi.org/10.1002/2017JB014408 |
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| Country | USA |
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