Nano-scale Experimental Investigation of In-situ Wettability and Spontaneous Imbibition in Ultra-tight Reservoir Rocks

The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization.

https://www.mdpi.com/2075-163X/9/3/183/pdf

X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods

X-ray computed tomography is a powerful non-destructive technique used in many domains to obtain the three-dimensional representation of objects, starting from the reconstitution of two-dimensional images of radiographic scanning. This technique is now able to analyze objects within a few micron resolutions. Consequently, X-ray microcomputed tomography opens perspectives for the analysis of the fabric of multiphase geomaterials such as soils, concretes, rocks and ceramics. To be able to characterize the spatial distribution of the different phases in such complex and disordered materials, automated phase recognition has to be implemented through image segmentation. A crucial difficulty in segmenting images lies in the presence of noise in the obtained tomographic representation, making it difficult to assign a specific phase to each voxel of the image. In the present study, simultaneous region growing is used to reconstitute the three-dimensional segmented image of granular materials. First, based on a set of expected phases in the image, regions where specific phases are sure to be present are identified, leaving uncertain regions of the image unidentified. Subsequently, the identified regions are grown until growing phases meet each other with vanishing unidentified regions. The method requires a limited number of manual parameters that are easily determined. The developed method is illustrated based on three applications on granular materials, comparing the phase volume fractions obtained by segmentation with macroscopic data. It is demonstrated that the algorithm rapidly converges and fills the image after a few iterations.

A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing

X-ray computed tomography data from chalk drill cuttings were taken over a series of voxel dimensions, ranging from 320 to 25 nm. From these data sets, standard petrophysical parameters (porosity, surface area, and permeability) were derived and we examined the effect of the voxel dimension (i.e., image resolution) on these properties. We found that for the higher voxel dimensions, they are severely over or underestimated, whereas for 50 and 25 nm voxel dimension, the resulting values (5%–30% porosity, 0.2–2 m2/g specific surface area, and 0.06–0.34 mD permeability) are within the expected range for this type of rock. We compared our results to macroscopic measurements and in the case of surface area, also to measurements using the Brunauer-Emmett-Teller (BET) method and found that independent of the degree of compaction, the results from tomography amount to about 30% of the BET method. Finally, we concluded that at 25 nm voxel dimension, the essential features of the nanoscopic pore network in chalk are captured but better resolution is still needed to derive surface area.

Resolution dependence of petrophysical parameters derived from X-ray tomography of chalk

When using image processing technology to analyze mineral particle size in complex scenes, it is difficult to separate the objects from the background with traditional algorithms. This paper proposes an ore image segmentation algorithm based on a histogram accumulation moment, which is applied to multi-scenario ore object location and recognition. Firstly, the multi-scale Retinex color restoration algorithm is used to improve the contrast in the dark region and eliminates the shadows generated by the stacked adhesion ores. Then, the zero-order and first-order cumulative moments close to the selected gray level are calculated, reducing the error caused by noise. Finally, the selected gray level gradually approaches the optimal threshold to avoid falling into local optimum. It can segment mineral images with unimodal or insignificant bimodal characteristic histogram effectively and accurately. Ore images in three different scenarios are used to verify the accuracy and effectiveness of the proposed method. The experimental results demonstrate that the proposed algorithm provides better segmentation results than other methods.

https://www.mdpi.com/2073-8994/11/3/431/pdf

S. Pedersen

Papers

Improved segmentation of X-ray tomography data from porous rocks using a dual filtering approach