China University of Petroleum

About: China University of Petroleum is a education organization based out in Beijing, China. It is known for research contribution in the topics: Catalysis & Oil shale. The organization has 39802 authors who have published 39151 publications receiving 483760 citations. The organization is also known as: Zhōngguó Shíyóu Dàxué & China University of Petroleum (Beijing).

Deep burial diagenesis and reservoir quality evolution of high-temperature, high-pressure sandstones: Examples from Lower Cretaceous Bashijiqike Formation in Keshen area, Kuqa depression, Tarim basin of China

Abstract: The deep high-temperature, high-pressure Lower Cretaceous Bashijiqike sandstone (buried to depths as great as 6.5-7.1km) is an important natural gas reservoir in Keshen gas field, Kuqa depression of the Tarim Basin. Reservoir quality is a critical risk factor in the development of these ultradeep reservoirs. Integrated approaches incorporating routine core analyses, mineralogical, petrographic, and geochemical analyses have been used to investigate the diagenetic history of these rocks and their impact on reservoir quality with the aim to unravel the mechanisms for maintaining anomalously high porosities in sandstones that are buried to such a great depth. These sandstones are dominantly fine to medium-grained, moderately to good sorted lithic arkoses and feldspathic litharenite. Most primary pores have been lost by mechanical compaction or carbonate cementation, and the reduction of porosity by mechanical compaction was more significant than by cementation. Dissolution of framework grains contributed to the enhancement of reservoir quality. Eogenetic diagenetic alterations mainly include mechanical compaction, precipitation of calcite cements and grain-coating clays; mesogenetic diagenesis is characterized by dissolution of framework grain by organic acids and subsequent precipitation of clay minerals and quartz; infiltration of meteoric water related to teleodiagenesis would result in dissolution of the framework grains. The meteoric leaching events during teleodiagenesis are of great importance for the Bashijiqike sandstones. Grain-coating clay minerals (mixed-layer illite/smectite clays) help to preserve porosity at depth by retarding quartz cementation and pressure solution. The unique burial regime as early-stage shallow burial with late-stage rapid deep burial contributes to porosity preservation in eodiagenesis. Fluid overpressure caused by intense structural compression in the middle Himalayan movement retarded compaction and helped preserve porosity in the late rapid deep burial stage. Anomalously high porosities are mainly found in medium-grained, good sorted sandstones with grain-coating clays, but with low clay and carbonate cement content, of which the porosity is preserved primarily and enhanced secondarily. The lowest porosities are associated with sandstones that are tightly compacted or cemented with carbonates or rich in detrital matrix. Porosity-depth trends may vary significantly with lithofacies due to its differences in textural and compositional attributes. Five lithofacies are defined in terms of detrital composition and texture, type and degree of diagenesis. The reservoir quality prediction models of various lithofacies are constructed, and the results of this study provide insights into mechanisms for maintaining anomalously high porosity and permeability in high-temperature, high-pressure sandstone reservoirs, and may help explain hydrocarbon distribution.

Covalently linked metal–organic framework (MOF)-polymer all-solid-state electrolyte membranes for room temperature high performance lithium batteries

Abstract: Extensive attention has been paid to metal–organic frameworks (MOFs) in the fields of gas storage/separation, sensors, catalysis, drug delivery and so on. However, the potential application of MOFs as electrolytes in lithium-ion batteries (LIBs) still needs to be further studied. In this study, showcased as the first example in the domain of all-solid-state electrolytes, a MOF covalently linked by flexible polymer chains toward a flexible stand-alone hybrid all-solid-state polymer electrolyte (HSPE) film is prepared by one-pot UV photopolymerization. Specifically, the precursors of the HSPE are composed of vinyl functionalized MOF (M-UiO-66-NH2) nanoparticles, poly(ethylene glycol) diacrylate (PEGDA), a lithium salt and a photoinitiator. It is confirmed that HSPE-1-8 (mM-UiO-66-NH2 : mPEGDA = 1 : 8) possesses over 5 times higher ionic conductivity (4.31 × 10−5 S cm−1 at 30 °C) and much better interfacial contact with Li electrodes than the SPE without incorporation of the vinyl functionalized MOF. Furthermore, at room temperature, the assembled cell of Li/HSPE-1-8/LiFePO4 exhibits outstanding rate capacity (it can reach 140, 124 and 80 mA h g−1 at 0.2, 0.5 and 1C, respectively) and excellent long cycle performance. Meanwhile, the solid cell can also be well run at 60 °C (the discharge capacity reaches 110 mA h g−1 at 2C). Combined with the superior low/high temperature performance of the cell, it is believed that the MOF-based hybrid SPE can be one of the most promising high-safety and high performance electrolytes for LIBs.

Fuzzy Rough Attribute Reduction for Categorical Data

Abstract: Classical rough set theory is considered a useful tool for dealing with the uncertainty of categorical data. The major deficiency of this model is that the classical rough set model is sensitive to noise in classification learning due to the stringent condition of equivalence relation. Thus, a class of fuzzy similarity relations was introduced to describe the similarity between samples with categorical attributes. However, these kinds of similarity relations also have deficiencies when they are used in fuzzy rough computation. In this article, we propose a new fuzzy-rough-set model for categorical data by introducing a variable parameter to control the similarity of samples. This model employs the iterative computation strategy to define fuzzy rough approximations and dependence functions. It is proved that the proposed rough dependence function is monotonic. Finally, the proposed model is applied to the attribute reduction of categorical data. The experimental results indicate that the proposed model is more effective for categorical data than some existing algorithms.