Baehyun Min (B Min)
Baehyun Min (B Min)
Department of Climate & Energy Systems Engineering, Ewha Womans University
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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
Pareto-based multi-objective history matching with respect to individual production performance in a heterogeneous reservoir
B Min, JM Kang, S Chung, C Park, I Jang
Journal of Petroleum Science and Engineering 122, 551-566, 2014
Optimal Well Placement Based on Artificial Neural Network Incorporating the Productivity Potential
B Min, C Park, JM Kang, HJ Park, IS Jang
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 33 …, 2011
Determination of an infill well placement using a data-driven multi-modal convolutional neural network
M Chu, B Min, S Kwon, G Park, S Kim, NX Huy
Journal of Petroleum Science and Engineering 195 (106805), 1-20, 2020
Optimal design of hydraulic fracturing in porous media using the phase field fracture model coupled with genetic algorithm
S Lee, B Min*, MF Wheeler
Computational Geosciences 22 (3), 833-849, 2018
Generation of synthetic density log data using deep learning algorithm at the Golden field in Alberta, Canada
S Kim, KH Kim, B Min, J Lim, K Lee
Geofluids 2020 (1), 5387183, 2020
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 (Part E), 109247, 2022
Determination of Oil Well Placement using Convolutional Neural Network Coupled with Robust Optimization under Geological Uncertainty
S Kwon, G Park, Y Jang, J Cho, M Chu, B Min
Journal of Petroleum Science and Engineering 201 (108118), 1-19, 2021
An optimal operation strategy of injection pressures in solvent-aided thermal recovery for viscous oil in sedimentary reservoirs
D Kam, C Park, B Min, JM Kang
Petroleum science and technology 31 (22), 2378-2387, 2013
Machine-Learning-Based Prediction of Oil Recovery Factor for Experimental CO2-Foam Chemical EOR: Implications for Carbon Utilization Projects
TH Vo, DS Dashtgoli, H Zhang, B Min
Energy 278, 127860, 2023
History Matching of a Channelized Reservoir using a Serial Denoising Autoencoder Integrated with ES-MDA.
S Kim, B Min, S Kwon, M Chu
Geofluids 2019, 3280961: 1-22, 2019
Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction
B Min, C Park, I Jang, JM Kang, S Chung
Applied Soft Computing 35, 75-112, 2015
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, 1532868: 1-21, 2018
Development of a robust multi-objective history matching for reliable well-based production forecasts
B Min, JM Kang, H Lee, S Jo, C Park, I Jang
Energy Exploration & Exploitation 34 (6), 795-809, 2016
Production-system optimization of gas fields using hybrid fuzzy/genetic approach
HJ Park, JS Lim, J Roh, JM Kang, B Min
SPE Journal 15 (02), 417-425, 2010
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), 1-26, 2020
Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
K Lee, S Kim, J Choi, B Min, HS Lee
Petroleum Science 16 (1), 124-147, 2019
Compositional modeling to analyze the effect of CH4 on coupled carbon storage and enhanced oil recovery process
J Cho, G Park, S Kwon, KS Lee, HS Lee, B Min
Applied Sciences 10 (12), 4272, 2020
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
Machine-learning-based water quality management of river with serial impoundments in the Republic of Korea
HW Lee, M Kim, HW Son, B Min, JH Choi
Journal of Hydrology: Regional Studies 41, 101069, 2022
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