Hung Vo Thanh 研究室

主宰者：Hung Vo Thanh

早稲田大学

AI 要約（直近 5 年の研究成果）

本研究室は、地下資源の探査・採掘・貯蔵に関わる複雑な現象を解明することを目指しています。特に、二酸化炭素の地下貯蔵、シェールガスの採取、地熱資源の開発、鉱山の水害防止といった課題に取り組んでいます。これらの課題に対して、地下の微細な孔隙構造における流体の挙動や、岩石内の熱・化学・力学的なプロセスを定量的に理解することが不可欠です。研究の手法として、分子動力学シミュレーションなどの微視的スケールの解析、医療用スキャナーで得た岩石画像を用いた数値シミュレーション、そして機械学習や深層学習といった人工知能技術を積極的に活用しています。特に深層学習では、従来の物理シミュレーションでは膨大な計算時間を要する問題を、学習済みモデルで高速に予測する工夫も行われています。これらの研究を通じて、複雑な地下環境における流体の挙動予測精度の向上、地層内の亀裂や断層ネットワークの自動検出、鉱山採掘時の水害のような地下災害の事前警戒などに関する知見を生み出しています。そうした知見は、安全で持続可能な資源開発と環境保全の両立に貢献することが期待されています。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

外部リンク

研究成果（100 件）

[2026] Artificial intelligence-driven monitoring network optimization under reservoir uncertainty for underground hydrogen storage
DOI: https://doi.org/10.1016/j.geoen.2026.214659
[2025] Characterizing seasonal submarine groundwater discharge patterns in Daya Bay, China using thermal infrared remote sensing
DOI: https://doi.org/10.1016/j.marpolbul.2025.118188
[2025] Advanced Optimized Deep-Learning Model for Precise Evaluation of Subsurface Carbon Dioxide Trapping Efficiency
DOI: https://doi.org/10.1021/acs.energyfuels.4c05843
[2025] Microscopic simulations of oil–water two-phase flow in high-porosity and low-permeability carbonate porous media
DOI: https://doi.org/10.1063/5.0266685
[2025] Revolutionizing hydrogen storage: Predictive modeling of hydrogen-brine interfacial tension using advanced machine learning and optimization technique
DOI: https://doi.org/10.1016/j.ijhydene.2025.04.231
[2025] Optimized neural networks for efficient modeling of crude oil production
DOI: https://doi.org/10.1007/s10115-025-02415-4
[2025] Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials
DOI: https://doi.org/10.1016/j.jallcom.2025.180709
[2025] Interpretable knowledge-guided machine learning for prediction wettability hydrogen-rocks/minerals-brine system in underground hydrogen storage project
DOI: https://doi.org/10.1016/j.ijhydene.2025.151462
[2025] CO2 and H2 behavior in kerogen matrix with CH4 presence: Molecular insights and implications
DOI: https://doi.org/10.1016/j.molliq.2025.128453
[2025] Advancing risk prediction of coal spontaneous combustion: Intelligent gas index models for accurate self-ignition temperature forecasting towards low-carbon energy systems
DOI: https://doi.org/10.1016/j.psep.2025.107676

続きを表示（残り 90 件）

[2025] Blasting Damage Control in Jointed Rock Tunnels: A Review with Numerical Validation of Water-Pressure Blasting
DOI: https://doi.org/10.3390/app152413187
[2025] Hybrid intelligent models for water activity prediction in complex ionic liquid systems
DOI: https://doi.org/10.1016/j.jece.2025.120358
[2025] Deep learning schemes for predicting deliverability from different underground gas storage reservoir types
DOI: https://doi.org/10.1016/j.jgsce.2025.205805
[2025] Artificial intelligence-driven forecast of methane recovery and CO2 storage efficiency for carbon-neutral energy production from marine gas hydrates
DOI: https://doi.org/10.1016/j.energy.2025.139141
[2025] Hybrid machine-learning and optimization models for precise determination of pore pressure changes in subsurface reservoirs
DOI: https://doi.org/10.1007/s10596-025-10387-1
[2025] Sequential framework integrating genetic algorithm and deep learning for multi-dimension fracture network characterization and uncertainty reduction
DOI: https://doi.org/10.1016/j.jhydrol.2025.134530
[2025] Deep Learning Driven Reservoir Simulation for Mapping Performance of CO2-EOR and Storage in Tight Oil Reservoir
DOI: https://doi.org/10.2118/227935-ms
[2025] A Molecular Perspective on Hydrogen Behavior in Moist Depleted Shale: Implications for Geological Storage
DOI: https://doi.org/10.2118/227958-ms
[2025] Second‐Order Degradation Modeling and Multiscale Feature Fusion for High‐Fidelity Segmentation of Low‐Quality Digital Rock Images
DOI: https://doi.org/10.1029/2025gl117039
[2025] Physics informed machine learning for prediction hydrogen solubility in aqueous solution to implication for underground hydrogen storage formations
DOI: https://doi.org/10.1016/j.renene.2025.124460
[2025] Enhanced prediction and uncertainty analysis for hydrogen production rate in depleted oil and gas reservoirs using advanced machine learning techniques
DOI: https://doi.org/10.1016/j.geoen.2025.213795
[2025] Fractal and NMR based characterizations of multi-scale pore structure alterations in tight sandstones due to scCO2-water–rock interactions
DOI: https://doi.org/10.1016/j.cej.2025.160898
[2025] Low-carbon advancement through cleaner production: A machine learning approach for enhanced hydrogen storage predictions in coal seams
DOI: https://doi.org/10.1016/j.renene.2025.122342
[2025] Data driven based deep learning for optimizing carbon storage and methane adsorption in unconventional shale gas reservoirs
DOI: https://doi.org/10.1016/j.jece.2025.116901
[2025] Machine-learning models for predicting CO2 solubility in various brine systems: implications for carbon geo-storage
DOI: https://doi.org/10.1016/j.molliq.2025.128122
[2025] X-band bandpass filter structure for passive radar using SIW technology
DOI: https://doi.org/10.54939/1859-1043.j.mst.104.2025.41-48
[2025] Sparse data-driven knowledge discovery for interpretable prediction of permeability in tight sandstones
DOI: https://doi.org/10.1016/j.enggeo.2025.108151
[2025] Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions
DOI: https://doi.org/10.1029/2025jh000703
[2024] Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach
DOI: https://doi.org/10.1016/j.ijhydene.2024.08.100
[2024] Environmental risk evaluation for radionuclide transport through natural barriers of nuclear waste disposal with multi-scale streamline approaches
DOI: https://doi.org/10.1016/j.scitotenv.2024.176084
[2024] Investigating steeply inclined abandoned mines for unlocking the secrets of water level recovery
DOI: https://doi.org/10.1038/s41598-024-82314-w
[2024] Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs
DOI: https://doi.org/10.1016/j.geoen.2024.213533
[2024] Transfer Learning with Transformer-Based Models for Mine Water Inrush Prediction: A Multivariate Analysis Using Sparse and Imbalanced Monitoring Data
DOI: https://doi.org/10.1007/s10230-024-01011-2
[2024] Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas
DOI: https://doi.org/10.1016/j.jare.2024.10.034
[2024] An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers
DOI: https://doi.org/10.1016/j.watres.2024.122706
[2024] Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models
DOI: https://doi.org/10.1016/j.petsci.2024.09.015
[2024] Investigate on spontaneous combustion characteristics of lignite stockpiles considering moisture and particle size effects
DOI: https://doi.org/10.1016/j.energy.2024.133193
[2024] Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks
DOI: https://doi.org/10.1007/s40948-024-00848-9
[2024] Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids
DOI: https://doi.org/10.1016/j.jii.2024.100662
[2024] Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems
DOI: https://doi.org/10.1016/j.jece.2024.113435
[2024] Developing a semi-analytical model for estimating mechanical properties of sandstone reservoirs: Enhancing applications in hydrocarbon production and underground gas storage
DOI: https://doi.org/10.1016/j.geoen.2024.213014
[2024] Analysis of damage evolution and study on mesoscopic damage constitutive model of granite under freeze–thaw cycling
DOI: https://doi.org/10.1007/s10064-024-03741-7
[2024] A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field
DOI: https://doi.org/10.1007/s11053-024-10350-4
[2024] Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs
DOI: https://doi.org/10.1007/s40948-024-00792-8
[2024] Retardation factor scaling for contaminant transport in fractured media
DOI: https://doi.org/10.1016/j.advwatres.2024.104709
[2024] Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study
DOI: https://doi.org/10.1016/j.eswa.2024.123944
[2024] Experimental study and application of similar materials in thick coal seam mining
DOI: https://doi.org/10.3389/fenrg.2024.1382444
[2024] Exploring hydrogen geologic storage in China for future energy: Opportunities and challenges
DOI: https://doi.org/10.1016/j.rser.2024.114366
[2024] Molecular Insights into Multiphase Transport through Realistic Kerogen-Based Nanopores
DOI: https://doi.org/10.1021/acs.energyfuels.3c04917
[2024] Identification of solute transport parameters in fractured granites with heterogeneous apertures
DOI: https://doi.org/10.1016/j.jhydrol.2024.130938
[2024] Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications
DOI: https://doi.org/10.1021/acs.energyfuels.3c04251
[2024] Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage
DOI: https://doi.org/10.1016/j.jece.2024.112210
[2024] Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models
DOI: https://doi.org/10.1016/j.jclepro.2024.141043
[2024] Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production
DOI: https://doi.org/10.1016/j.ijhydene.2024.01.115
[2024] Inter-layer interference for multi-layered tight gas reservoir in the absence and presence of movable water
DOI: https://doi.org/10.1016/j.petsci.2024.01.009
[2024] Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications
DOI: https://doi.org/10.1038/s41598-023-50853-3
[2024] Unraveling Overlying Rock Fracturing Evolvement for Mining Water Inflow Channel Prediction: A Spatiotemporal Analysis Using ConvLSTM Image Reconstruction
DOI: https://doi.org/10.1109/tgrs.2024.3452937
[2024] Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM
DOI: https://doi.org/10.1109/tgrs.2024.3384990
[2023] Coupling Genetic algorithm and Random Forest for robust prediction of CO2 storage efficiency in underground formations
DOI: https://doi.org/10.3997/2214-4609.202310147
[2023] Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage
DOI: https://doi.org/10.1016/j.ijhydene.2023.12.131
[2023] Development of an in-situ gel from CO2-captured complex solution and inhibiting coal spontaneous combustion: A case study in thermal engineering
DOI: https://doi.org/10.1016/j.csite.2023.103423
[2023] Investigating the influence of bentonite colloids on strontium sorption in granite under various hydrogeochemical conditions
DOI: https://doi.org/10.1016/j.scitotenv.2023.165819
[2023] Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques
DOI: https://doi.org/10.1016/j.energy.2023.128546
[2023] Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model
DOI: https://doi.org/10.3390/en16052348
[2023] A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors
DOI: https://doi.org/10.1016/j.jclepro.2023.136606
[2023] Depositional and diagenetic modeling of the Margala Hill Limestone, Hazara area (Pakistan): Implications for reservoir characterization using outcrop analogues
DOI: https://doi.org/10.1016/j.geoen.2023.211584
[2023] Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations
DOI: https://doi.org/10.1016/j.asoc.2023.110408
[2023] Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites
DOI: https://doi.org/10.1016/j.eswa.2023.119796
[2023] Upscaling dispersivity for conservative solute transport in naturally fractured media
DOI: https://doi.org/10.1016/j.watres.2023.119844
[2023] Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage
DOI: https://doi.org/10.1016/j.scitotenv.2023.162944
[2023] Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme
DOI: https://doi.org/10.1016/j.fuel.2023.128183
[2023] Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
DOI: https://doi.org/10.1016/j.seppur.2023.123807
[2023] Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects
DOI: https://doi.org/10.1016/j.energy.2023.127860
[2023] Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management
DOI: https://doi.org/10.1016/j.scitotenv.2023.162056
[2023] Quantitative Characterization of Shallow Marine Sediments in Tight Gas Fields of Middle Indus Basin: A Rational Approach of Multiple Rock Physics Diagnostic Models
DOI: https://doi.org/10.3390/pr11020323
[2023] A Deep Learning-Based Data-Driven Approach for Predicting Mining Water Inrush From Coal Seam Floor Using Microseismic Monitoring Data
DOI: https://doi.org/10.1109/tgrs.2023.3300012
[2022] Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project
DOI: https://doi.org/10.1016/j.jclepro.2022.133778
[2022] CO<sub>2</sub> Sequestration and Enhanced Oil Recovery via the Water Alternating Gas Scheme in a Mixed Transgressive Sandstone-Carbonate Reservoir: Case Study of a Large Middle East Oilfield
DOI: https://doi.org/10.1021/acs.energyfuels.2c02185
[2022] Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models
DOI: https://doi.org/10.1002/ese3.1276
[2022] Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan
DOI: https://doi.org/10.1016/j.geogeo.2022.100123
[2022] Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan
DOI: https://doi.org/10.3390/su14169835
[2022] Paleoenvironmental and Bio-Sequence Stratigraphic Analysis of the Cretaceous Pelagic Carbonates of Eastern Tethys, Sulaiman Range, Pakistan
DOI: https://doi.org/10.3390/min12080946
[2022] Correction: 3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam
DOI: https://doi.org/10.1007/s13202-022-01517-x
[2022] Developing the efficiency-modeling framework to explore the potential of CO2 storage capacity of S3 reservoir, Tahe oilfield, China
DOI: https://doi.org/10.1007/s40948-022-00434-x
[2022] Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals
DOI: https://doi.org/10.1016/j.egyr.2022.06.003
[2022] Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques
DOI: https://doi.org/10.1029/2021wr031048
[2022] Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers
DOI: https://doi.org/10.1016/j.apenergy.2022.118985
[2022] Prediction of Cretaceous reservoir zone through petrophysical modeling: Insights from Kadanwari gas field, Middle Indus Basin
DOI: https://doi.org/10.1016/j.geogeo.2022.100058
[2022] Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping
DOI: https://doi.org/10.1016/j.fuel.2022.123821
[2022] Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis
DOI: https://doi.org/10.3390/min13010029
[2022] Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory
DOI: https://doi.org/10.1007/s11356-022-24326-5
[2022] Experimental study of moisture effects on spontaneous combustion of Baiyinhua lignite from individual particles to stockpile
DOI: https://doi.org/10.1016/j.fuel.2022.126774
[2022] An optimized neuro-fuzzy system using advance nature-inspired Aquila and Salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations
DOI: https://doi.org/10.1016/j.est.2022.106150
[2022] Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting
DOI: https://doi.org/10.3390/e24111674
[2022] MarbleNet: A Deep Neural Network Solution for Vietnamese Voice Activity Detection
DOI: https://doi.org/10.1109/nics56915.2022.10013457
[2022] Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling
DOI: https://doi.org/10.1021/acsomega.2c05759
[2022] Diagenetic overprint on porosity and permeability of a combined conventional-unconventional reservoir: Insights from the Eocene pelagic limestones, Gulf of Suez, Egypt
DOI: https://doi.org/10.1016/j.marpetgeo.2022.105967
[2022] Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites
DOI: https://doi.org/10.1016/j.renene.2022.09.132
[2022] Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin
DOI: https://doi.org/10.3389/feart.2022.1014052
[2022] Sand-ratio distribution in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos Basin: Acoustic impedance inversion-based reservoir quality prediction
DOI: https://doi.org/10.3389/feart.2022.1018105

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AI 要約（直近 5 年の研究成果）

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研究成果（100 件）

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