Zhijiang Kang

880 total citations
40 papers, 654 citations indexed

About

Zhijiang Kang is a scholar working on Ocean Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Zhijiang Kang has authored 40 papers receiving a total of 654 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Ocean Engineering, 23 papers in Mechanical Engineering and 16 papers in Mechanics of Materials. Recurrent topics in Zhijiang Kang's work include Hydraulic Fracturing and Reservoir Analysis (23 papers), Reservoir Engineering and Simulation Methods (17 papers) and Hydrocarbon exploration and reservoir analysis (14 papers). Zhijiang Kang is often cited by papers focused on Hydraulic Fracturing and Reservoir Analysis (23 papers), Reservoir Engineering and Simulation Methods (17 papers) and Hydrocarbon exploration and reservoir analysis (14 papers). Zhijiang Kang collaborates with scholars based in China, United States and Canada. Zhijiang Kang's co-authors include Yu‐Shu Wu, Perapon Fakcharoenphol, Di Yuan, Xiansong Zhang, Hui Zhao, Albert C. Reynolds, Lin Cao, Richard E. Ewing, Guan Qin and Yalchin Efendiev and has published in prestigious journals such as Journal of Cleaner Production, Water Resources Research and International Journal of Rock Mechanics and Mining Sciences.

In The Last Decade

Zhijiang Kang

37 papers receiving 634 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhijiang Kang China 13 445 417 216 96 69 40 654
Piyang Liu China 12 412 0.9× 368 0.9× 148 0.7× 161 1.7× 83 1.2× 53 655
Célio Maschio Brazil 16 863 1.9× 708 1.7× 115 0.5× 109 1.1× 26 0.4× 87 984
Didier Yu Ding France 17 796 1.8× 754 1.8× 261 1.2× 186 1.9× 49 0.7× 50 996
Dominique Guérillot France 15 545 1.2× 324 0.8× 216 1.0× 272 2.8× 101 1.5× 70 799
Ingeborg Skjelkvåle Ligaarden Norway 6 274 0.6× 215 0.5× 101 0.5× 205 2.1× 138 2.0× 7 505
Hongquan Chen United States 15 329 0.7× 288 0.7× 88 0.4× 81 0.8× 67 1.0× 80 582
Mohan Kelkar United States 21 1.1k 2.4× 842 2.0× 240 1.1× 223 2.3× 106 1.5× 122 1.3k
Hyung Kwak United States 14 362 0.8× 372 0.9× 243 1.1× 194 2.0× 35 0.5× 86 642
Adwait Chawathé United States 14 505 1.1× 389 0.9× 221 1.0× 56 0.6× 14 0.2× 43 606
James R. Gilman United States 13 868 2.0× 915 2.2× 227 1.1× 317 3.3× 45 0.7× 35 1.1k

Countries citing papers authored by Zhijiang Kang

Since Specialization
Citations

This map shows the geographic impact of Zhijiang Kang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zhijiang Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhijiang Kang more than expected).

Fields of papers citing papers by Zhijiang Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhijiang Kang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zhijiang Kang. The network helps show where Zhijiang Kang may publish in the future.

Co-authorship network of co-authors of Zhijiang Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Zhijiang Kang. A scholar is included among the top collaborators of Zhijiang Kang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zhijiang Kang. Zhijiang Kang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kang, Zhijiang, et al.. (2025). Sweet spots discrimination in carbonate reservoirs based on weakly supervised learning. Geoenergy Science and Engineering. 252. 213965–213965.
2.
Li, Yanyan, Zhihong Kang, Haifeng Liu, et al.. (2025). A method to consider capillary end effect in relative permeability curves. Journal of Cleaner Production. 493. 144906–144906. 1 indexed citations
3.
Kang, Zhijiang, et al.. (2024). Influence of tectonic stress field on oil and gas migration in the Tahe Oilfield carbonate reservoir: Identification of areas favorable for reservoir formation. International Journal of Rock Mechanics and Mining Sciences. 180. 105829–105829. 3 indexed citations
4.
Deng, Z. Y., et al.. (2024). Deep learning-based dynamic forecasting method and application for ultra-deep fractured reservoir production. Frontiers in Energy Research. 12. 4 indexed citations
5.
Ma, Yongsheng, Xunyu Cai, Maowen Li, et al.. (2024). Research advances on the mechanisms of reservoir formation and hydrocarbon accumulation and the oil and gas development methods of deep and ultra-deep marine carbonates. Petroleum Exploration and Development. 51(4). 795–812. 22 indexed citations
6.
Kang, Zhijiang, et al.. (2023). A novel semi-analytical model for transient pressure behavior in fracture-cave carbonate reservoirs. Geoenergy Science and Engineering. 228. 211921–211921. 3 indexed citations
7.
Rui, Hongxing, et al.. (2023). A reduced-order model based on C-R mixed finite element and POD technique for coupled Stokes-Darcy system with solute transport. Computational Geosciences. 28(5). 821–832. 3 indexed citations
8.
Liu, Huiqing, et al.. (2023). Impact of effective stress on permeability for carbonate fractured-vuggy rocks. Journal of Rock Mechanics and Geotechnical Engineering. 16(3). 942–960. 12 indexed citations
9.
Zhang, Dongmei, et al.. (2023). Research on seismic hydrocarbon prediction based on a self-attention semi-supervised model. Geoenergy Science and Engineering. 226. 211808–211808. 4 indexed citations
10.
11.
Li, Sanbai, Zhijiang Kang, & Yun Zhang. (2023). Numerical Modeling and Simulation of Fractured-Vuggy Reservoirs Based on Field Outcrops. Water. 15(20). 3687–3687. 2 indexed citations
12.
Liu, Huiqing, Juliana Y. Leung, Min Yang, et al.. (2022). Investigation on water-drive performance of a fault-karst carbonate reservoir under different well patterns and injection-production modes based on 2D visualized physical models. Journal of Petroleum Science and Engineering. 218. 110925–110925. 4 indexed citations
13.
Wang, Jing, Huiqing Liu, Jing Zhang, et al.. (2018). Experiments on the influences of well pattern on water flooding characteristics of dissolution vug-cave reservoir. Petroleum Exploration and Development. 45(6). 1103–1111. 6 indexed citations
14.
Zhang, Dongmei, et al.. (2017). Efficient history matching with dimensionality reduction methods for reservoir simulations. SIMULATION. 94(8). 739–751. 8 indexed citations
15.
Zhao, Hui, et al.. (2016). A Physics-Based Data-Driven Numerical Model for Reservoir History Matching and Prediction With a Field Application. SPE Journal. 21(6). 2175–2194. 80 indexed citations
16.
17.
Wu, Yu‐Shu, Di Yuan, Zhijiang Kang, & Perapon Fakcharoenphol. (2011). A multiple-continuum model for simulating single-phase and multiphase flow in naturally fractured vuggy reservoirs. Journal of Petroleum Science and Engineering. 78(1). 13–22. 125 indexed citations
18.
Kang, Zhijiang. (2010). New Method of Coupling Numerical Simulation and Application to Fracture-Cavern Carbonate Reservoir. Xinjiang shiyou dizhi. 4 indexed citations
19.
Popov, Peter, Guan Qin, Yalchin Efendiev, et al.. (2007). Multiscale Methods for Modeling Fluid Flow Through Naturally FracturedCarbonate Karst Reservoirs. Proceedings of SPE Annual Technical Conference and Exhibition. 9 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026