EXPERIMENTAL INVESTIGATION OF HYDRAULIC FRACTURING FLUID ON SHALE AND SOIL

Mitigation and prevention of shale-formation damage caused by hydraulic-fracturing fluid/rock interactions play an important role in well-production stability and subsequent refracturing design. This study presents three experimental investigations on the interaction of water/shale, fluid/clay, and fluid/shale. A series of experiments were designed to investigate fluid/shale interactions: hydrophilic to hydrophobic alteration through chemical-vapor deposition, nanoindentation testing on shale sample, geotechnical laboratory experiments on contaminated clay, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and scanning electron microscope (SEM) on shale sample. A clay-matrix-based data-screening criterion is proposed for nanoindentation. The continuous-stiffness-measurment (CSM) method is proved to have better definition and characterization of softening of shale based on the proposed criterion. This study furthered the numerical model of clay deformation by Hattab and Chang (2015) by considering different pore fluid concentration. The fracturing fluid contaminated clay produced changes of geotechnical properties. Based on the proposed criterion and designed experiments, fracturing fluid contaminated shale was observed to gain 4 to 6% of NaCl. However, all other minerals contents are found to decrease after the shale powder-fluid interaction. A characteristic depth was proposed to consider reduction of hardness and mineral content at the same time. Moreover, an empirical equation was proposed to describe fracture toughness of shale by using a selection of indentation depth, its corresponding hardness and Young’s modulus

Study of Minimum Void Ratio for Soils with a Range of Grain-Size Distributions

Minimum void ratio or maximum packing density is an important soil property in geotechnical engineering. It apply to volume change tendency control, fluid conductivity control and particles movement. Previous researchers have attempted to predict maximum packing density by empirical/graphic method, rock correction method, alpha method. Based on the concepts of F. de Larrard in concrete mixture research, we have developed a mathematic model that can predict the minimum void ratio for soils with a wide range of particle size. Probability density function-lognormal distribution was tested and used to provide a reasonable representation for soils with a range of grain-size distribution. We incorporate the lognormal distribution in the mathematical model, and predict the minimum void ratio for various types of soil gradations. The validity of the model is evaluated. The evaluation of the model is also performed on several sets of data in the literature, which include binary packing system of steel balls, ternary packing system of spheres, mixtures of round and crushed aggregates, and soils containing gravelly sand with silt. Comparison of the results will be discussed

SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images

Semantic segmentation is a challenging computer vision task demanding a significant amount of pixel-level annotated data. Producing such data is a time-consuming and costly process, especially for domains with a scarcity of experts, such as medicine or forensic anthropology. While numerous semi-supervised approaches have been developed to make the most from the limited labeled data and ample amount of unlabeled data, domain-specific real-world datasets often have characteristics that both reduce the effectiveness of off-the-shelf state-of-the-art methods and also provide opportunities to create new methods that exploit these characteristics. We propose and evaluate a semi-supervised method that reuses available labels for unlabeled images of a dataset by exploiting existing similarities, while dynamically weighting the impact of these reused labels in the training process. We evaluate our method on a large dataset of human decomposition images and find that our method, while conceptually simple, outperforms state-of-the-art consistency and pseudo-labeling-based methods for the segmentation of this dataset. This paper includes graphic content of human decomposition