Algorithms for 3D Particles Characterization Using X-Ray Microtomography in Proppant Crush Test

Influence of Surface Wettability of Nonhomogeneous Proppant on Hydraulic Fracture Inflow Performance

Quartz sand proppant is widely used in hydraulic fracturing and the extraction of low-permeability reservoirs to prevent fracture closure and enhance reservoir recovery effectively. The influence of proppant size and type on well productivity has been widely studied, but the mechanism of proppant surface wettability on the hydraulic fracture inflow performance has not been thoroughly investigated. To further understand the influence of proppant wettability on fracture inflow performance, in this work, a hydrophobic quartz sand proppant was prepared by a simple dip-coating method using silane solution with a static water contact angle of 136.8° in air, and three types of filtration layer models were prepared according to the mass ratio of hydrophilic/hydrophobic particles, hydrophilic (4:0), hybrid (2:2), and hydrophobic (0:4), to analyze the effects of quartz sand proppant wettability on the oil/water distribution and conductivity performance in the three filter layers. The preliminary results show that (1) under different mesh sizes of quartz sand, different emulsion concentrations, and high-viscosity oil samples, the hydraulic performance of the hydrophobic quartz sand filter layer is generally better than that of the hydrophilic quartz sand filter layer; and (2) the hydraulic performance of the hydrophobic quartz sand filter layer decreases as the concentration of the emulsion increases, the hybrid quartz sand filter layer is basically unaffected and its hydraulic performance is relatively stable, and the hydraulic performance of the hydrophilic quartz sand filter layer is more fluctuating. The flow conductivity of the hydrophilic quartz sand filter layer fluctuates greatly. In addition, the mechanism of the influence of proppant wettability on the flow-conducting performance is proposed through the microscopic percolation experiment of an oil-water emulsion. This study is of some guiding significance for engineers to better design the surface wettability of proppant and the selection and matching of proppant in hydraulic fracturing construction design to improve the recovery rate of low-permeability reservoirs.

A Review of Particle Size Analysis with X-ray CT

Particle size and morphology analysis is a problem common to a wide range of applications, including additive manufacturing, geological and agricultural materials' characterisation, food manufacturing and pharmaceuticals. Here, we review the use of microfocus X-ray computed tomography (X-ray CT) for particle analysis. We give an overview of different sample preparation methods, image processing protocols, the morphology parameters that can be determined, and types of materials that are suitable for analysis of particle sizes using X-ray CT. The main conclusion is that size and shape parameters can be determined for particles larger than approximately 2 to 3 μm, given adequate resolution of the X-ray CT setup. Particles composed of high atomic number materials (Z > 40) require careful sample preparation to ensure X-ray transmission. Problems occur when particles with a broad range of sizes are closely packed together, or when particles are fused (sintered or cemented). The use of X-ray CT for particle size analysis promises to become increasingly widespread, offering measurements of size, shape, and porosity of large numbers of particles within one X-ray CT scan.

A Review of Watershed Implementations for Segmentation of Volumetric Images

Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm-watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.

Morphological description of microplastic particles for environmental fate studies

The morphological description of microplastic particles is mostly based on subjective descriptors. However, data intercomparison requires unambiguous classifications. This work presents a morphological description based on the lengths of the smallest enclosing orthogonal parallelepiped. Three dimensionless parameters, namely equancy, platiness and elongation describe any particle shape with reference on the basic 3D (sphere), 2D (plate) and 1D (rod) shapes. The particle size directly linked to the environmental fate of microplastics is the Stoke's diameter. The derivation of Stoke's diameter based on 3D morphological descriptors is explained and the proxies that can be used if only 2D projected images are available is discussed. This work shows that the behaviour of irregular particles is not adequately predicted using as descriptor the diameter of the sphere with the same volume as the particle. There is a need to obtain equations specifically developed for plastic particles, especially for fibres, and for the atmospheric compartment.

An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples

Image segmentation is a crucial step of almost any Digital Rock workflow. In this paper, we propose an approach for generation of a labelled dataset and investigate an application of three popular convolutional neural networks (CNN) architectures for segmentation of 3D microtomographic images of samples of various rocks. Our dataset contains eight pairs of images of five specimens of sand and sandstones. For each sample, we obtain a single set of microtomographic shadow projections, but run reconstruction twice: one regular high-quality reconstruction, and one using just a quarter of all available shadow projections. Thoughtful manual Indicator Kriging (IK) segmentation of the full-quality image is used as the ground truth for segmentation of images with reduced quality. We assess the generalization capability of CNN by splitting our dataset into training and validation sets by five different manners. In addition, we compare neural networks results with segmentation by IK and thresholding. Segmentation outcomes by 2D and 3D U-nets are comparable to IK, but the deep neural networks operate in automatic mode, and there is big room for improvements in solutions based on CNN. The main difficulties are associated with the segmentation of fine structures that are relatively uncommon in our dataset.