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Kai Zhang

Researcher at China University of Petroleum

Publications -  414
Citations -  6148

Kai Zhang is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 31, co-authored 303 publications receiving 3787 citations. Previous affiliations of Kai Zhang include Wuhan University of Science and Technology & Shandong University.

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Review of nanofluids for heat transfer applications

TL;DR: In this article, a critical review of research on heat transfer applications of nanofluids with the aim of identifying the limiting factors so as to push forward their further development is presented.
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Supercritical fluids technology for clean biofuel production

TL;DR: In this paper, the authors reviewed state-of-the-art application of the supercritical fluid (SCF) technique in biofuels production that includes biodiesel from vegetable oils via the transesterification process, bio-hydrogen from the gasification and bio-oil from the liquefaction of biomass, with biodiesel production as the main focus.
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Adsorption behaviors of shale oil in kerogen slit by molecular simulation

TL;DR: In this article, a molecular dynamic simulation had been performed to quantify the adsorption behavior of shale oil in kerogen slits, and both the distribution of shaleoil properties and potential of the mean force (PMF) were used to identify the interaction mechanisms between the light and heavy components respectively represented by methane and asphaltene.
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Pore-scale simulation of shale oil flow based on pore network model

TL;DR: In this paper, a pore network model is developed based on a modified shale oil flow equation to consider those combinational effects on shale oil permeability under different organic matter contents, and then is applied to provide an order analysis of those effects on the permeability of a representative shale model.
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Well production forecasting based on ARIMA-LSTM model considering manual operations

TL;DR: A novel hybrid model is established that considers the advantages of linearity and nonlinearity, as well as the impact of manual operations, that integrates the autoregressive integrated moving average (ARIMA) model and the long short term memory (LSTM) model.