Honggeun Jo

606 total citations · 1 hit paper
27 papers, 436 citations indexed

About

Honggeun Jo is a scholar working on Ocean Engineering, Mechanical Engineering and Geophysics. According to data from OpenAlex, Honggeun Jo has authored 27 papers receiving a total of 436 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Ocean Engineering, 16 papers in Mechanical Engineering and 11 papers in Geophysics. Recurrent topics in Honggeun Jo's work include Reservoir Engineering and Simulation Methods (22 papers), Hydraulic Fracturing and Reservoir Analysis (16 papers) and Seismic Imaging and Inversion Techniques (11 papers). Honggeun Jo is often cited by papers focused on Reservoir Engineering and Simulation Methods (22 papers), Hydraulic Fracturing and Reservoir Analysis (16 papers) and Seismic Imaging and Inversion Techniques (11 papers). Honggeun Jo collaborates with scholars based in United States, South Korea and Puerto Rico. Honggeun Jo's co-authors include Michael J. Pyrcz, Javier E. Santos, Christopher J. Landry, Maša Prodanović, Duo Xu, Hyungsik Jung, Jonggeun Choe, Kyungbook Lee, Sungil Kim and Wen Pan and has published in prestigious journals such as Geophysics, AAPG Bulletin and Advances in Water Resources.

In The Last Decade

Honggeun Jo

26 papers receiving 424 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media

2020 178 citations Javier E. Santos, Duo Xu et al. Advances in Water Resources profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Honggeun Jo United States	11	283	182	112	96	95	27	436
Meng Tang United States	8	351 1.2×	244 1.3×	56 0.5×	227 2.4×	94 1.0×	13	626
Peter Tilke United States	10	157 0.6×	101 0.6×	95 0.8×	65 0.7×	72 0.8×	32	381
Mohammad Emami Niri Iran	12	207 0.7×	166 0.9×	90 0.8×	60 0.6×	109 1.1×	51	354
Zhijiang Kang China	13	445 1.6×	417 2.3×	216 1.9×	96 1.0×	69 0.7×	40	654
Fatimah Alzubaidi Australia	6	172 0.6×	176 1.0×	142 1.3×	36 0.4×	68 0.7×	7	342
Amie Hows United States	9	221 0.8×	182 1.0×	190 1.7×	36 0.4×	104 1.1×	17	363
Ramin Soltanmohammadi United States	9	158 0.6×	137 0.8×	130 1.2×	33 0.3×	48 0.5×	16	331
Yufu Niu Australia	10	196 0.7×	104 0.6×	146 1.3×	36 0.4×	90 0.9×	16	364
Hongquan Chen United States	15	329 1.2×	288 1.6×	88 0.8×	81 0.8×	34 0.4×	80	582
Zhi Chai United States	15	401 1.4×	391 2.1×	202 1.8×	77 0.8×	100 1.1×	24	590

Countries citing papers authored by Honggeun Jo

Since Specialization

Citations

This map shows the geographic impact of Honggeun Jo'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 Honggeun Jo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Honggeun Jo more than expected).

Fields of papers citing papers by Honggeun Jo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Honggeun Jo. 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 Honggeun Jo. The network helps show where Honggeun Jo may publish in the future.

Co-authorship network of co-authors of Honggeun Jo

This figure shows the co-authorship network connecting the top 25 collaborators of Honggeun Jo. A scholar is included among the top collaborators of Honggeun Jo 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 Honggeun Jo. Honggeun Jo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Jo, Honggeun, et al.. (2025). Minimum acceptance criteria for subsurface scenario-based uncertainty models from single image generative adversarial networks (SinGAN). Computational Geosciences. 29(1). 2 indexed citations

Jo, Honggeun, et al.. (2024). Sequential binary classification of lithofacies from well-log data and their uncertainty quantification. Interpretation. 12(4). T573–T584. 1 indexed citations

Kim, H. Alicia, et al.. (2024). Machine Learning-based 4-D Seismic Data Integration and Characterization of Channelized Anticline Aquifer for Geological Carbon Sequestration. Journal of the Korean Society of Mineral and Energy Resources Engineers. 61(1). 1–14. 1 indexed citations

Jo, Honggeun, et al.. (2024). Field-scale SAGD Performance Evaluation Utilizing Homogeneous Reservoir Model Based on Vertical Wells. Journal of the Korean Society of Mineral and Energy Resources Engineers. 61(3). 208–221.

Kim, Dogyun, et al.. (2024). Improved Injection Schedules of CO2 for Pohang Basin, Yeongil Bay, South Korea: Regarding Field Security and Injection Effectiveness. Lithosphere. 2024(4). 1 indexed citations

Jo, Honggeun, et al.. (2023). Sensitivity analysis of geological rule-based subsurface model parameters on fluid flow. AAPG Bulletin. 107(6). 887–906. 1 indexed citations

Tang, Hewei, Pengcheng Fu, Honggeun Jo, et al.. (2022). Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR. International journal of greenhouse gas control. 120. 103765–103765. 24 indexed citations

Jo, Honggeun, et al.. (2022). Machine-learning-based porosity estimation from multifrequency poststack seismic data. Geophysics. 87(5). M217–M233. 11 indexed citations

Tang, Hewei, Pengcheng Fu, Honggeun Jo, et al.. (2022). Deep Learning-Accelerated 3d Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from Insar. SSRN Electronic Journal. 1 indexed citations

10.

Pan, Wen, Honggeun Jo, Javier E. Santos, Carlos Torres‐Verdín, & Michael J. Pyrcz. (2022). Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling. AAPG Bulletin. 106(11). 2163–2186. 4 indexed citations

11.

Yu, Yanxiang, Chicheng Xu, Siddharth Misra, et al.. (2021). Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods. Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description. 62(4). 393–406. 21 indexed citations

12.

Jo, Honggeun & Michael J. Pyrcz. (2021). Automatic Semivariogram Modeling by Convolutional Neural Network. Mathematical Geosciences. 54(1). 177–205. 18 indexed citations

13.

Jo, Honggeun, Wen Pan, Javier E. Santos, Hyungsik Jung, & Michael J. Pyrcz. (2021). Machine learning assisted history matching for a deepwater lobe system. Journal of Petroleum Science and Engineering. 207. 109086–109086. 23 indexed citations

14.

Jo, Honggeun, Javier E. Santos, & Michael J. Pyrcz. (2020). Conditioning well data to rule-based lobe model by machine learning with a generative adversarial network. Energy Exploration & Exploitation. 38(6). 2558–2578. 25 indexed citations

15.

Santos, Javier E., Duo Xu, Honggeun Jo, et al.. (2020). PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media. Advances in Water Resources. 138. 103539–103539. 178 indexed citations breakdown →

16.

Jo, Honggeun & Michael J. Pyrcz. (2019). Robust Rule-Based Aggradational Lobe Reservoir Models. Natural Resources Research. 29(2). 1193–1213. 5 indexed citations

17.

Jo, Honggeun, Javier E. Santos, & Michael J. Pyrcz. (2019). Conditioning Stratigraphic, Rule-Based Models with Generative Adversarial Networks: A Deepwater Lobe, Deep Learning Example. 3 indexed citations

18.

Jung, Hyungsik, Honggeun Jo, Sungil Kim, Kyungbook Lee, & Jonggeun Choe. (2018). Geological model sampling using PCA-assisted support vector machine for reliable channel reservoir characterization. Journal of Petroleum Science and Engineering. 167. 396–405. 34 indexed citations

19.

Jung, Hyungsik, Honggeun Jo, Kyungbook Lee, & Jonggeun Choe. (2017). Characterization of Various Channel Fields Using an Initial Ensemble Selection Schemeand Covariance Localization. Journal of Energy Resources Technology. 139(6). 10 indexed citations

20.

Jo, Honggeun, et al.. (2016). History matching of channel reservoirs using ensemble Kalman filter with continuous update of channel information. Energy Exploration & Exploitation. 35(1). 3–23. 23 indexed citations

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.

Explore authors with similar magnitude of impact