| Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network Z Zhong, AY Sun, H Jeong Water Resources Research 55 (7), 5830-5851, 2019 | 177 | 2019 |
| Fast evaluation of well placements in heterogeneous reservoir models using machine learning A Nwachukwu, H Jeong, M Pyrcz, LW Lake Journal of Petroleum Science and Engineering 163, 463-475, 2018 | 148 | 2018 |
| A learning-based data-driven forecast approach for predicting future reservoir performance H Jeong, AY Sun, J Lee, B Min Advances in Water Resources 118, 95-109, 2018 | 72 | 2018 |
| Machine learning-based optimization of well locations and WAG parameters under geologic uncertainty A Nwachukwu, H Jeong, A Sun, M Pyrcz, LW Lake SPE Improved Oil Recovery Conference?, D031S008R005, 2018 | 68 | 2018 |
| Building complex event processing capability for intelligent environmental monitoring AY Sun, Z Zhong, H Jeong, Q Yang Environmental modelling & software 116, 1-6, 2019 | 51 | 2019 |
| Improvement of ensemble smoother with clustered covariance for channelized reservoirs K Lee, H Jeong, SP Jung, J Choe Energy Exploration & Exploitation 31 (5), 713-726, 2013 | 46 | 2013 |
| Characterization of channelized reservoir using ensemble Kalman filter with clustered covariance K Lee, H Jeong, SP Jung, J Choe Energy Exploration & Exploitation 31 (1), 17-29, 2013 | 42 | 2013 |
| Efficient deep-learning-based history matching for fluvial channel reservoirs S Jo, H Jeong, B Min, C Park, Y Kim, S Kwon, A Sun Journal of Petroleum Science and Engineering 208, 109247, 2022 | 35 | 2022 |
| Reservoir characterization using an EnKF and a non-parametric approach for highly non-Gaussian permeability fields Y Shin, H Jeong, J Choe Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 32 …, 2010 | 33 | 2010 |
| Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review B Liang, J Liu, J You, J Jia, Y Pan, H Jeong Fuel 337, 127067, 2023 | 31 | 2023 |
| Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis B Kang, H Jung, H Jeong, J Choe Petroleum Science 17 (1), 182-195, 2020 | 31 | 2020 |
| Cost-optimal design of pressure-based monitoring networks for carbon sequestration projects, with consideration of geological uncertainty H Jeong, AY Sun, X Zhang International Journal of Greenhouse Gas Control 71, 278-292, 2018 | 29 | 2018 |
| Fast assessment of CO2 plume characteristics using a connectivity based proxy H Jeong, S Srinivasan International Journal of Greenhouse Gas Control 49, 387-412, 2016 | 28 | 2016 |
| Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning AY Sun, H Jeong, A González-Nicolás, TC Templeton Computers & geosciences 113, 70-80, 2018 | 26 | 2018 |
| Reservoir characterization from insufficient static data using gradual deformation method with ensemble Kalman filter H Jeong, S Ki, J Choe Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 32 …, 2010 | 25 | 2010 |
| Utilization of multiobjective optimization for pulse testing dataset from a CO2-EOR/sequestration field B Min, AY Sun, MF Wheeler, H Jeong Journal of Petroleum Science and Engineering 170, 244-266, 2018 | 23 | 2018 |
| Integration of an Iterative Update of Sparse Geologic Dictionaries with ES‐MDA for History Matching of Channelized Reservoirs S Kim, B Min, K Lee, H Jeong Geofluids 2018 (1), 1532868, 2018 | 21 | 2018 |
| Influence of injection strategies on local capillary trapping during geological carbon sequestration in saline aquifers B Ren, H Jeong Journal of CO2 Utilization 27, 441-449, 2018 | 20 | 2018 |
| Development of ensemble smoother–neural network and its application to history matching of channelized reservoirs S Kim, K Lee, J Lim, H Jeong, B Min Journal of Petroleum Science and Engineering 191, 107159, 2020 | 19 | 2020 |
| Uncertainty Quantification of CO2 Plume Migration Using Static Connectivity of Geologic Features H Jeong, S Srinivasan, S Bryant Energy Procedia 37, 3771-3779, 2013 | 18 | 2013 |
