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Yiteng Li

Bio: Yiteng Li is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 9, co-authored 21 publications receiving 184 citations.

Papers
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Journal ArticleDOI
TL;DR: The first self-adaptive deep learning algorithm is proposed in details to accelerate flash calculations, which can quantitatively predict the total number of phases in the mixture and related thermodynamic properties at equilibrium for realistic reservoir fluids with a large number of components under various environmental conditions.

60 citations

Journal ArticleDOI
TL;DR: In this article, the authors applied the Grand Canonical Monte Carlo and molecular dynamics simulations to understand the molecular mechanism of ion and water transport in montmorillonite clays as a function of relative humidity (RH).
Abstract: Grand canonical Monte Carlo and molecular dynamics simulations were applied to understand the molecular mechanism of ion and water transport in montmorillonite clays as a function of relative humidity (RH). The variation of basal spacings of montmorillonite as a function of RH predicted based on the swelling free energy profiles was consistent with X-ray data. The hydration of the montmorillonite shows the following well-known order: Mg2+ > Ca2+ > Sr2+ > Li+ > Na+ > K+. The relative contribution of water on external surfaces only becomes significant close to the saturation pressure. However, this behavior for K-montmorillonite starts to occur well below the saturation pressure due to the clay-swelling inhibition by potassium ions. The diffusion of water and ions generally increases with RH in all samples. However, for samples with weakly hydrated ions, the water mobility in thin films adsorbed on external basal surfaces of clay can be higher than that in the water-saturated mesopores. For a given RH, mobi...

51 citations

Journal ArticleDOI
TL;DR: An NVT flash calculation scheme is established with a thermodynamically stable evolution algorithm to generate training and testing data for the proposed deep neural network, and the trained model can be used for various fluid mixtures, which significantly accelerates flash calculations in unconventional reservoirs.

36 citations

Journal ArticleDOI
TL;DR: This paper presents a parallel and fully implicit simulator for the black oil model based on the variational inequality (VI) framework, which can be used to enforce important mathematical and physical properties to obtain accurate constraint-preserving solutions.

30 citations

Journal ArticleDOI
TL;DR: A scalable fully implicit solver is introduced for the simulation of two-phase flow in a porous medium with capillarity, gravity and compressibility, which is free from the limitations of the conventional methods.

29 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

01 Jan 2016

1,715 citations

01 Jan 1991
TL;DR: In this paper, a review of the literature available outside the petroleum industry concerning wettability is presented, including van der Waal and electrostatic forces, as well as hydrogen bonding and ion/water interactions.
Abstract: Review of the litterature available outside the petroleum industry concerning wettability. This litterature discusses surface forces and their effects on wettability, including van der Waal and electrostatic forces. Hydrogen bonding and ion/water interactions, are also discussed. Advancing and receding contact angles for oil/water/mineral systems in terms of these surface forces are described.

359 citations

Journal ArticleDOI
TL;DR: In this paper, a novel tailor-made TiO2 nanoparticles-bentonite (TNBT) nanocomposite-enhanced drilling mud was tested, which showed excellent performance; lubricity was enhanced, thin and well-intact mud cakes were created, coating was provided on shale and bentonite plugs, and clay and shale swelling were mitigated.

70 citations

Journal ArticleDOI
TL;DR: The first self-adaptive deep learning algorithm is proposed in details to accelerate flash calculations, which can quantitatively predict the total number of phases in the mixture and related thermodynamic properties at equilibrium for realistic reservoir fluids with a large number of components under various environmental conditions.

60 citations