World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
73
Citations
19455
World Ranking
864
National Ranking
153

Jun Yao publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Jun Yao sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 846 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Jun Yao D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Jun Yao sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 73 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Jun Yao is affiliated with the China University of Petroleum, Beijing in China. Their research contributes primarily to the field of Engineering, with a focus on multiple subfields including Ocean Engineering, Mechanical Engineering, Mechanics of Materials, Environmental Engineering, and Computational Mechanics.

The scientist's work covers a range of topics centered on petroleum and reservoir sciences. These include Hydraulic Fracturing and Reservoir Analysis, Enhanced Oil Recovery Techniques, Hydrocarbon Exploration and Reservoir Analysis, Drilling and Well Engineering, Reservoir Engineering and Simulation Methods, CO2 Sequestration and Geologic Interactions, and Groundwater Flow and Contamination Studies.

Jun Yao has coauthored numerous publications with several frequent collaborators. Notable coauthors include Yongfei Yang, Hai Sun, Kai Zhang, Lei Zhang, and Junjie Zhong.

The scientist's research finds publication in a variety of specialized venues, with frequent contributions to:

  • SSRN Electronic Journal
  • Journal of Petroleum Science and Engineering
  • SPE Journal
  • Physics of Fluids
  • arXiv (Cornell University)

Jun Yao's notable recent papers include:

  • "Well production forecasting based on ARIMA-LSTM model considering manual operations," 2020, Energy
  • "Adsorption behaviors of shale oil in kerogen slit by molecular simulation," 2020, Chemical Engineering Journal
  • "Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems," 2020, IEEE Transactions on Cybernetics
  • "Recent Advances and Future Perspectives in Carbon Capture, Transportation, Utilization, and Storage (CCTUS) Technologies: A Comprehensive Review," 2023, Fuel
  • "Training effective deep reinforcement learning agents for real-time life-cycle production optimization," 2021, Journal of Petroleum Science and Engineering

Best Publications

  • Numerical simulation of the heat extraction in EGS with thermal-hydraulic-mechanical coupling method based on discrete fractures model

    Zhi-xue Sun;Xu Zhang;Yi Xu;Jun Yao

  • Nanoscale simulation of shale transport properties using the lattice Boltzmann method: permeability and diffusivity

    Li Chen;Lei Zhang;Qinjun Kang;Hari S. Viswanathan

  • Well production forecasting based on ARIMA-LSTM model considering manual operations

    Dongyan Fan;Dongyan Fan;Hai Sun;Hai Sun;Jun Yao;Kai Zhang

  • New pore space characterization method of shale matrix formation by considering organic and inorganic pores

    Yongfei Yang;Jun Yao;Chenchen Wang;Ying Gao

  • Apparent gas permeability in an organic-rich shale reservoir

    Wenhui Song;Jun Yao;Yang Li;Hai Sun

  • Adsorption behaviors of shale oil in kerogen slit by molecular simulation

    Yongfei Yang;Jie Liu;Jun Yao;Jianlong Kou

  • Numerical simulation of the heat extraction in 3D-EGS with thermal-hydraulic-mechanical coupling method based on discrete fractures model

    Jun Yao;Xu Zhang;Zhixue Sun;Zhaoqin Huang

  • Pore-scale modeling: Effects of wettability on waterflood oil recovery

    Xiucai Zhao;Xiucai Zhao;Martin J. Blunt;Jun Yao

  • Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems

    Xiaoming Xue;Kai Zhang;Kay Chen Tan;Liang Feng

  • Effect of surface chemistry for CH4/CO2 adsorption in kerogen: A molecular simulation study

    Hongguang Sui;Jun Yao

  • Recent Advances and Future Perspectives in Carbon Capture, Transportation, Utilization, and Storage (CCTUS) Technologies: A Comprehensive Review

    Unknown

  • Nanoscale simulation of shale transport properties using the lattice Boltzmann method: permeability and diffusivity

    Li Chen;Lei Zhang;Qinjun Kang;Jun Yao

  • Numerical study of CO2 enhanced natural gas recovery and sequestration in shale gas reservoirs

    Hai Sun;Jun Yao;Sun-hua Gao;Dong-yan Fan

  • The Effect of Wettability Heterogeneity on Relative Permeability of Two-Phase Flow in Porous Media: A Lattice Boltzmann Study

    Jianlin Zhao;Qinjun Kang;Jun Yao;Hari Viswanathan

  • Flow Behaviors of Shale Oil in Kerogen Slit by Molecular Dynamics Simulation

    Unknown

  • Dynamic Pore‐Scale Dissolution by CO 2 ‐Saturated Brine in Carbonates: Impact of Homogeneous Versus Fractured Versus Vuggy Pore Structure

    Yongfei Yang;Yingwen Li;Jun Yao;Stefan Iglauer

  • An empirical study of bandwidth predictability in mobile computing

    Jun Yao;Salil S. Kanhere;Mahbub Hassan

  • Thermodynamically consistent modelling of two-phase flows with moving contact line and soluble surfactants

    Guangpu Zhu;Jisheng Kou;Bowen Yao;Yu Shu Wu

  • Electrostatics of the Granular Flow in a Pneumatic Conveying System

    Jun Yao;Yan Zhang;Chi-Hwa Wang;Shuji Matsusaka

  • Training effective deep reinforcement learning agents for real-time life-cycle production optimization

    Kai Zhang;Kai Zhang;Zhongzheng Wang;Guodong Chen;Liming Zhang

  • An efficient embedded discrete fracture model based on mimetic finite difference method

    Xia Yan;Zhaoqin Huang;Jun Yao;Yang Li

  • Numerical simulation of gas transport mechanisms in tight shale gas reservoirs

    Jun Yao;Hai Sun;Dong-yan Fan;Chen-chen Wang

  • History Matching of Naturally Fractured Reservoirs Using a Deep Sparse Autoencoder

    Kai Zhang;Jinding Zhang;Xiaopeng Ma;Chuanjin Yao

  • Characterization of gas transport behaviors in shale gas and tight gas reservoirs by digital rock analysis

    Hai Sun;Jun Yao;Ying-chang Cao;Dong-yan Fan

Frequent Co-Authors

Yongfei Yang
Yongfei Yang China University of Petroleum (East China)
Pengyuan Yang
Pengyuan Yang Fudan University
Shuyu Sun
Shuyu Sun King Abdullah University of Science and Technology
Kun-Liang Guan
Kun-Liang Guan Westlake University
Mingzhe Dong
Mingzhe Dong University of Calgary
Zhangxin Chen
Zhangxin Chen University of Calgary
Yue Xiong
Yue Xiong University of North Carolina at Chapel Hill
Qinjun Kang
Qinjun Kang Los Alamos National Laboratory
Yu-Shu Wu
Yu-Shu Wu Colorado School of Mines
Jintu Fan
Jintu Fan Hong Kong Polytechnic University

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