World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
4561
World Ranking
9230
National Ranking
2590

John Killough 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 John Killough 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: 166 publications — 34th percentile

34% of scientists in this discipline score the same or lower.

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

John Killough 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 John Killough 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: 34 D-Index — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Overview

John Killough is affiliated with Texas A&M University in the United States and has a research focus centered on engineering, particularly within several specialized subfields. Their work spans ocean engineering, mechanical engineering, mechanics of materials, computational mechanics, and statistical and nonlinear physics.

Their research covers a range of topics related to hydraulic fracturing and reservoir analysis, drilling and well engineering, hydrocarbon exploration and reservoir analysis, reservoir engineering and simulation methods, lattice Boltzmann simulation studies, model reduction and neural networks, and enhanced oil recovery techniques.

Killough has contributed scholarly articles to several publication venues, with multiple works appearing in the Journal of Petroleum Science and Engineering, arXiv, the SPE Annual Technical Conference and Exhibition, the Chemical Engineering Journal, and Fuel.

  • A transient two-phase flow model for production prediction of tight gas wells with fracturing fluid-induced formation damage (2021, Journal of Petroleum Science and Engineering)
  • Integrated characterization of the fracture network in fractured shale gas Reservoirs-Stochastic fracture modeling, simulation and assisted history matching (2021, Journal of Petroleum Science and Engineering)
  • Lattice Boltzmann simulation of phase equilibrium of methane in nanopores under effects of adsorption (2021, Chemical Engineering Journal)
  • Investigating the effects of stress creep and effective stress coefficient on stress-dependent permeability measurements of shale rock (2020, Journal of Petroleum Science and Engineering)
  • Effect of vertical heterogeneity and nano-confinement on the recovery performance of oil-rich shale reservoir (2020, Fuel)

Frequent co-authors collaborating with Killough include Jungang Chen, Eduardo Gildin, Jingwei Huang, Yu Jiang, and Yonghui Wu.

The main research topics in Killough's work extensively cover:

  • Hydraulic Fracturing and Reservoir Analysis
  • Drilling and Well Engineering
  • Hydrocarbon exploration and reservoir analysis
  • Reservoir Engineering and Simulation Methods
  • Lattice Boltzmann Simulation Studies
  • Model Reduction and Neural Networks
  • Enhanced Oil Recovery Techniques

Their publications reveal a focus on modeling and simulation approaches addressing complex phenomena in shale gas reservoirs, multiphase fluid flow, stress-dependent permeability in shale rock, and nanoscale adsorption effects, relevant to oil and gas industry applications.

Best Publications

  • Reservoir Simulation With History-Dependent Saturation Functions

    J.E. Killough

  • Beyond dual-porosity modeling for the simulation of complex flow mechanisms in shale reservoirs

    Bicheng Yan;Yuhe Wang;John E. Killough

  • Ninth SPE Comparative Solution Project: A Reexamination of Black-Oil Simulation

    J.E. Killough

  • Fifth Comparative Solution Project: Evaluation of Miscible Flood Simulators

    J. E. Killough;C. A. Kossack

  • Compositional Modeling of Tight Oil Using Dynamic Nanopore Properties

    Yuhe Wang;Bicheng Yan;John Killough

  • An efficient method for fractured shale reservoir history matching: The embedded discrete fracture multi-continuum approach

    Z. Chai;B. Yan;J.E. Killough;Y. Wang

  • Simulation of Compositional Reservoir Phenomena on a Distributed-Memory Parallel Computer

    J.E. Killough;Rao Bhogeswara

  • History Matching Using the Method of Gradients: Two Case Studies

    R.C. Bissell;Yogeshwar Sharma;J.E. Killough

  • Numerical investigation of gas flow rate in shale gas reservoirs with nanoporous media

    Hongqing Song;Hongqing Song;Mingxu Yu;Weiyao Zhu;Peng Wu

  • Parallel Iterative Linear Equation Solvers: An Investigation of Domain Decomposition Algorithms for Reservoir Simulation

    J.E. Killough;M.F. Wheeler

  • Numerical Investigation of Effects of Subsequent Parent-Well Injection on Interwell Fracturing Interference Using Reservoir-Geomechanics-Fracturing Modeling

    Xuyang Guo;Kan Wu;Cheng An;Jizhou Tang

  • A New Approach for the Simulation of Fluid Flow in Unconventional Reservoirs through Multiple Permeability Modeling

    Bicheng Yan;Masoud Alfi;Yuhe Wang;John Edwin Killough

  • A transient two-phase flow model for production prediction of tight gas wells with fracturing fluid-induced formation damage

    Yonghui Wu;Yonghui Wu;Linsong Cheng;Liqiang Ma;Shijun Huang

  • Computed tomography imaging of air sparging in porous media

    May-Ru Chen;Richard E. Hinkley;John E. Killough

  • Impact of permeability heterogeneity on production characteristics in water-bearing tight gas reservoirs with threshold pressure gradient

    Hongqing Song;Hongqing Song;Yang Cao;Mingxu Yu;Yuhe Wang

  • Integrated characterization of the fracture network in fractured shale gas Reservoirs—Stochastic fracture modeling, simulation and assisted history matching

    Yonghui Wu;Yonghui Wu;Yonghui Wu;Linsong Cheng;John Killough;Shijun Huang

  • Investigation of Production-Induced Stress Changes for Infill-Well Stimulation in Eagle Ford Shale

    Xuyang Guo;Kan Wu;John Killough

  • A fully compositional model considering the effect of nanopores in tight oil reservoirs

    Bicheng Yan;Yuhe Wang;John E. Killough

  • An analytical method for modeling and analysis gas-water relative permeability in nanoscale pores with interfacial effects

    Tianxin Li;Hongqing Song;Hongqing Song;Jiulong Wang;Yuhe Wang

  • Evaluation of CO2 injection into shale gas reservoirs considering dispersed distribution of kerogen

    Jingwei Huang;Tianying Jin;Maria Barrufet;John Killough

  • General Multi-Porosity simulation for fractured reservoir modeling

    Bicheng Yan;Masoud Alfi;Cheng An;Yang Cao

  • Uncertainty Quantification of the Fracture Network with a Novel Fractured Reservoir Forward Model

    Zhi Chai;Hewei Tang;Youwei He;John Killough

  • Analyzing the Well-Interference Phenomenon in the Eagle Ford Shale/Austin Chalk Production System With a Comprehensive Compositional Reservoir Model

    Hewei Tang;Bicheng Yan;Zhi Chai;Lihua Zuo

  • Investigating the pressure characteristics and production performance of liquid-loaded horizontal wells in unconventional gas reservoirs

    Hewei Tang;Zhuang Sun;Youwei He;Youwei He;Zhi Chai

  • A Transient Two-Phase Flow Model for Production Prediction of Tight Gas Wells with Fracturing Fluid-Induced Formation Damage

    Yonghui Wu;Linsong Cheng;Shijun Huang;Sidong Fang

Frequent Co-Authors

Kamy Sepehrnoori
Kamy Sepehrnoori The University of Texas at Austin
Matthew T. Balhoff
Matthew T. Balhoff The University of Texas at Austin
Timothy J. Kneafsey
Timothy J. Kneafsey Lawrence Berkeley National Laboratory
Vamegh Rasouli
Vamegh Rasouli University of Wyoming
George J. Moridis
George J. Moridis Lawrence Berkeley National Laboratory
Mary F. Wheeler
Mary F. Wheeler The University of Texas at Austin
Keliu Wu
Keliu Wu China University of Petroleum, Beijing

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