Liquid Storage Characteristics of Nanoporous Particles in Shale: Rigorous Proof

Different from conventional reservoirs, a significant proportion of oil is in an adsorbed or even immobile state in shale and tight rocks. There are established comprehensive mathematical models quantifying the adsorbed, immobile, and free oil contents in shale rocks. However, the conclusions of the monotonicity of the complicated models from sensitivity analysis might not be universal, and rigorous mathematical derivation is needed to demonstrate their rationale. In this paper, the models for oil/water storage in the nanoporous grains in shale, i.e., kerogen and clay, are achieved based on the aforementioned storage models. Rigorous analytical derivations are employed to strictly prove the monotonicity of the immobile and adsorbed models, which is the main purpose of this work. This work expands the applicability of the storage models, is fundamental and important for mobility analysis in shale reservoirs, and can shed light on its efficient exploration and development

Investigation on Roles of Packing Density and Water Film Thickness in Synergistic Effects of Slag and Silica Fume

The ternary blended cement with finer slag and silica fume (SF) could improve the packing density (PD) through the filling effect. The excess water (water more than needed for filling into voids between the cement particles) can be released to improve the fresh properties and densify the microstructure which is beneficial for improving the hardened properties. To verify the hypothesis and reveal how and why (cement + slag + SF) the ternary blends could bring such advantages, the binder pastes incorporating slag and SF with various water-to-binder ratios were produced to determine the PD experimentally. To evaluate the optimum water demand (OWD) for maximum wet density, the influence of the dispersion state of the binder on PD was investigated using the wet packing density approach. The effect of PD of various binary and ternary binder systems on water film thickness (WFT), fluidity, setting time, and compressive strength development of cement paste was also investigated. The results show that the ternary blends could improve the PD and decrease the water film thickness (WFT). The enhanced PD and altered WFT are able to increase fluidity and compressive strength. The ternary blends could improve the compressive strengths by increasing PD and exerting nucleation and pozzolanic effects

Role of ITZ in the degradation process of blended cement concrete under magnesium sulfate attack

Although the importance of the interfacial transition zone (ITZ) to sulfate attack is obvious, the relation between the ITZ and the rate of degradation is still unclear. The impact of ITZ volume content on the degradation of concrete, including length, mass, and elastic dynamic modulus (Ed) variation, fully immersed in magnesium sulfate solution were investigated in this work. The microstructure was analyzed by backscattered electron imaging (BSE) and energy dispersive X-ray spectroscopy (EDX) mapping analysis. The performance of concrete exposed to magnesium sulfate highly depended on the aggregate and the composition of raw materials. There was a trend toward more serious deterioration with increasing ITZ in the reference and limestone filler blended group after 12 months of exposure. The degradation of specimens made with slag was independent of the variation of ITZ content. Gypsum tended to precipitate in the ITZ under magnesium sulfate attack. The EDX analysis confirmed the decomposition of C-S-H, accompanied by the formation of degradation products M-S-H. (c) 2020 American Society of Civil Engineers

Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers

Lower-extremity peripheral artery disease (LE-PAD) is a prevalent circulatory disorder with risks of critical limb ischemia and amputation. This study aimed to develop a prediction model for a novel LE-PAD subtype to predict the severity of the disease and guide personalized interventions. Additionally, LE-PAD pathogenesis involves altered immune microenvironment, we examined the immune differences to elucidate LE-PAD pathogenesis. A total of 460 patients with LE-PAD were enrolled and clustered using unsupervised machine learning algorithms (UMLAs). Logistic regression analyses were performed to screen and identify predictive factors for the novel subtype of LE-PAD and a prediction model was built. We performed a comparative analysis regarding neutrophil levels in different subgroups of patients and an immune cell infiltration analysis to explore the associations between neutrophil levels and LE-PAD. Through hematoxylin and eosin (H&E) staining of lower-extremity arteries, neutrophil infiltration in patients with and without LE-PAD was compared. We found that UMLAs can helped in constructing a prediction model for patients with novel LE-PAD subtypes which enabled risk stratification for patients with LE-PAD using routinely available clinical data to assist clinical decision-making and improve personalized management for patients with LE-PAD. Additionally, the results indicated the critical role of neutrophil infiltration in LE-PAD pathogenesis

Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies

Properly initializing land snow conditions with multi-satellite data assimilation (DA) may help tackle the long-standing challenge of Asian monsoon seasonal forecasts. However, to what extent can snow DA help resolve the problem remains largely unexplored. Here we establish, for the first time, that improved springtime snow initializations assimilating the Moderate Spectral Imaging Satellite (MODIS) snow cover fraction and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data can improve the simulation accuracy of Asian monsoon seasonal anomalies. Focusing on the western Tibetan Plateau (TP) and mid- to high-latitude Eurasia (EA), two regions where multi-satellite snow DA is critical, we found that DA influences the monsoon circulation at different months depending on the regional snow–atmosphere coupling strengths. For the pre-monsoon season, accurate initialization of the TP snow is key, and assimilating MODIS data slightly outperforms jointly assimilating MODIS and GRACE data. For the peak-monsoon season, accurate initialization of the EA snow is more important due to its long memory, and assimilating GRACE data brings the most pronounced gains. Among all the Asian monsoon subregions, the most robust improvement is seen over central north India, a likely result of the region’s strong sensitivity to thermal forcing. While this study highlights complementary snow observations as promising new sources of the monsoon predictability, it also clarifies complexities in translating DA to useful monsoon forecast skill, which may help bridge the gap between land DA and dynamical climate forecasting studies