High-Quality MicroCT Rock Imaging: Methodology To Measure and Correct for X-Ray Scatter
- Alexander Katsevich (iTomography Corporation and University of Central Florida) | Michael Frenkel (iTomography Corporation) | Qiushi Sun (Aramco Services Company: Aramco Research Center - Houston) | Shannon L. Eichmann (Aramco Services Company: Aramco Research Center - Houston) | Victor Prieto (iTomography Corporation)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- February 2020
- Document Type
- Journal Paper
- 226 - 241
- 2020.Society of Petroleum Engineers
- shale cores, digital rock physics, computed tomography, CT, microCT core imaging, X-ray scatter
- 4 in the last 30 days
- 59 since 2007
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Microcomputed tomography (microCT) of cores yields valuable information about rock and fluid properties at pore scale for conventional rock and at rock heterogeneity scale for unconventionals. High levels of uncorrected X-ray scatter in computed tomography (CT) data lead to strong image artifacts and erroneous Hounsfield unit (HU) values, making reconstructed images unsuitable for accurate digital rock (DR) characterization (e.g., segmentation, material decomposition, and others). MicroCT scanners do not include scatter correction techniques. To fill this gap, we developed a new methodology to measure and remove the scatter component from raw projection microCT data collected during rock core scans, and ultimately improving the image quality of scanned cores.
Widely used approaches for scatter estimation, based on Monte Carlo (MC) simulations and simplified analytical models, are time-consuming and may lose accuracy when imaging complex unconventional shale cores. In this paper, we propose a more practical approach to perform scatter correction from direct scatter measurements, an approach that is based on the beam-stop array (BSA) method. The BSA method works as follows: The radiation scattered by the core sample is emitted in random directions. By placing an array of small, highly absorbing beads between the source and the core, the primary X-ray signal through the beads is blocked, but the overall object scatter signal is not affected. The observed values in the beads' shadows on the detector are assumed to be scatter signal. Performing interpolation of the scatter signal between the shadowed by beads pixels on the detector gives an estimate of the scatter signal at every pixel on the detector. Subtracting scatter from projection data yields scatter-corrected data used for 3D CT core image reconstruction.
To develop the core scatter correction methodology, we executed the following three tasks: (1) performed modeling of primary and scattered signals to optimize the BSA design (beads size and layout) and scan parameters; (2) developed and implemented an accurate scatter correction algorithm into our 3D microCT image reconstruction workflow; and (3) tested the proposed methodology using four shale core samples from the United States and the Middle East.
To better assess the impact of scatter, all experiments with shale core plugs presented in this paper were conducted using source energy of 160 kVp. Our results demonstrated that in many cases, especially with higher attenuating cores, failing to correct for X-ray scatter may result in significant loss of image reconstruction accuracy. We also showed that the developed methodology allows for accurate estimation and removal of scatter from the raw (projection) CT data, enabling reconstruction of high-quality core images that are required for performing DR analysis.
To assess the impact of X-ray scatter on the accuracy of DR segmentation, we compared the amount of resolved air-filled space using a stack of image slices by thresholding for the air regions. Our results showed that the amount of detected air-filled space may increase significantly when scatter correction is applied.
The presented scatter correction methodology is general and can be used with any microCT scanner used by the petroleum industry to improve image quality and derive accurate HU values. This is of significant importance for quantitative characterization of highly heterogeneous rock with fine structural changes, as is the case for shale. Ultimately, this methodology should expand the operational envelope and value of microCT imaging in the exploration and production workflows.
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