2024/02/28 更新

写真a

ヴォ タン フン
ヴォ タン フン
所属
理工学術院 理工学術院総合研究所
職名
次席研究員(研究院講師)
メールアドレス
メールアドレス

経歴

  • 2023年10月
    -
    継続中

    早稲田大学   理工学術院 理工学術院総合研究所

  • 2020年12月
    -
    2023年06月

    ソウル大学   地球環境科学部

学歴

  • 2017年10月
    -
    2020年09月

    九州大学  

  • 2015年08月
    -
    2017年08月

    Universitas Gadjah Mada   Geological Engineering   Master of Engineering  

    Petroleum Geoscience

  • 2008年10月
    -
    2013年04月

    Vietnam National University   Petroleum and Geology Engineering   Bachelor of Engineering  

    Drilling and Production Petroleum Engineering

委員歴

  • 2021年01月
    -
    継続中

    Journal of Petroleum Exploration and Production Technology, Springer  Associate Editor

所属学協会

  • 2019年05月
    -
    継続中

    European Association of Geoscientists and Engineers

  • 2008年12月
    -
    継続中

    Society of Petroleum Engineers

研究分野

  • 地球資源工学、エネルギー学   CCUS, Underground hydrogen storage, Energy transition, Smart Reservoir Simulation, Machine Learning, Artificial Intelligence

研究キーワード

  • 地球規模の炭素貯蔵

  • 最適化アルゴリズム

  • データサイエンス

  • スマート貯留層シミュレーション

  • 地下水素貯蔵

  • エネルギー遷移

  • 機械学習

  • 二酸化炭素回収の利用と貯蔵

▼全件表示

受賞

  • Brain Pool fellowship for invitation oversea talent scientist

    2022年06月   National Research Foundation of Korea (NRF)   Research Fellowship  

  • BrainKoea21 for young talent discovery pioneer engineering

    2020年11月   South Korea Government   Postdoc fellowship  

  • Travel grant for excellent conference paper

    2019年06月   European Association of Geoscientists & Engineers   81st EAGE Conference and Exhibition 2019  

 

論文

  • Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production

    Hung Vo Thanh, Zhenxue Dai, Zhengyang Du, Huichao Yin, Bicheng Yan, Mohamad Reza Soltanian, Ting Xiao, Brian McPherson, Laith Abualigah

    International Journal of Hydrogen Energy   57   1000 - 1009  2024年02月

     概要を見る

    Hydrogen offers significant potential as a sustainable energy source. However, its storage and transportation pose challenges due to its volatility and low density. Subsurface geological formations, such as shale, sandstone, and coal, have been investigated as potential storage sites for hydrogen. Precise estimation of hydrogen adsorption in these formations necessitates a thorough understanding of kerogens, organic-rich sedimentary rocks prevalent in unconventional formations. In this study, we introduce a novel approach employing Extreme Gradient Boosting (XGBoost), Random forest (RF), Light gradient Boosting (LGBM), and Natural gradient boosting (NGBM) algorithms to intelligently predict hydrogen adsorption in various kerogen types. Among these, the NGBM algorithm, utilizing three input features (temperature, pressure, and kerogen types), demonstrated the highest predictive accuracy, achieving an R2 value of 0.989, RMSE of 0.062, and MAE of 0.037 for all data samples. SHAP diagram analysis identified kerogen types as the most influential parameter within the NGBM model. The presented approach has implications beyond energy storage, highlighting the significance of advanced technologies in addressing complex energy challenges. Precise hydrogen adsorption estimation in subsurface formations is vital for developing sustainable energy solutions, and our approach has the potential to expedite progress in this field. Interdisciplinary collaboration among geologists, chemists, and data scientists is crucial for devising innovative solutions for sustainable energy.

    DOI

    Scopus

  • Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage

    Hung Vo Thanh, Hemeng Zhang, Zhenxue Dai, Tao Zhang, Suparit Tangparitkul, Baehyun Min

    International Journal of Hydrogen Energy   55   1422 - 1433  2024年02月

     概要を見る

    Hydrogen is a clean and sustainable renewable energy source with significant potential for use in energy storage applications because of its high energy density. In particular, underground hydrogen storage via the dissolution of hydrogen gas in an aqueous solution has been identified as a promising strategy to address the difficulties associated with large-scale energy storage. However, this process requires the accurate prediction of the solubility of hydrogen in aqueous solutions, which is affected by a range of factors, including temperature, pressure, and the presence of solutes. The present study thus aimed to effectively predict the solubility of hydrogen in aqueous solutions that vary in their salinity by employing a machine learning approach. Four machine learning models were developed and tested: adaptive gradient boosting (Adaboost), gradient boosting, random forest, and extreme gradient boosting. The performance of each model was quantified in terms of their coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The Adaboost algorithm exhibited superior performance across all metrics, with an R2 of 0.994, MAE of 0.006, and RMSE of 0.018. A Williams plot detected only 18 outliers in the Adaboost predictions from a total of 255 data points. These results indicate that machine learning techniques have the potential to serve as a valuable tool in the prediction of hydrogen solubility in aqueous solutions for underground hydrogen storage, facilitating the development of smart, cost-effective, and safe hydrogen storage technologies.

    DOI

    Scopus

    2
    被引用数
    (Scopus)
  • Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models

    Hemeng Zhang, Pengcheng Wang, Mohammad Rahimi, Hung Vo Thanh, Yongjun Wang, Zhenxue Dai, Qian Zheng, Yong Cao

    Journal of Cleaner Production    2024年02月

    DOI

    Scopus

    1
    被引用数
    (Scopus)
  • Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques

    Mohammad Rahimi, Hossein Mashhadimoslem, Hung Vo Thanh, Benyamin Ranjbar, Mobin Safarzadeh Khosrowshahi, Abbas Rohani, Ali Elkamel

    Energy    2023年11月

    DOI

    Scopus

    5
    被引用数
    (Scopus)
  • Machine-learning-based prediction of oil recovery factor for experimental CO<inf>2</inf>-Foam chemical EOR: Implications for carbon utilization projects

    Hung Vo Thanh, Danial Sheini Dashtgoli, Hemeng Zhang, Baehyun Min

    Energy   278  2023年09月

     概要を見る

    Enhanced oil recovery (EOR) using CO2 injection is promising with economic and environmental benefits as an active climate-change mitigation approach. Nevertheless, the low sweep efficiency of CO2 injection remains a challenge. CO2-foam injection has been proposed as a remedy, but its laboratory screening for specific reservoirs is costly and time-consuming. In this study, machine-learning models are employed to predict oil recovery factor (ORF) during CO2-foam flooding cost-effectively and accurately. Four models, including general regression neural network (GRNN), cascade forward neural network with Levenberg–Marquardt optimization (CFNN-LM), cascade forward neural network with Bayesian regularization (CFNN-BR), and extreme gradient boosting (XGBoost), are evaluated based on experimental data from previous studies. Results demonstrate that the GRNN model outperforms the others, with an overall mean absolute error of 0.059 and an R2 of 0.9999. The GRNN model's applicability domain is verified using a Williams plot, and an uncertainty analysis for CO2-foam flooding projects is conducted. The novelty of this study lies in developing a machine-learning-based approach that provides an accurate and cost-effective prediction of ORF in CO2-foam experiments. This approach has the potential to significantly reduce screening costs and time required for CO2-foam injection, making it a more viable carbon utilization and EOR strategy.

    DOI

    Scopus

    10
    被引用数
    (Scopus)
  • Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme

    Hung Vo Thanh, Mohammad Rahimi, Zhenxue Dai, Hemeng Zhang, Tao Zhang

    Fuel   345  2023年08月

     概要を見る

    This study explores the use of machine learning algorithms to predict hydrogen wettability in underground storage sites. The motivation for this research is the need to find a safe and efficient way to store hydrogen, which has become increasingly important as the world shifts toward using more clean energy sources. The study used four different machine learning algorithms including XGBoost, RF, LGRB, and Adaboost_DT to analyze 513 data points collected from previous literature. The input features included pressure, temperature, salinity, and substrate types, while the target output was hydrogen wettability. This study found that the XGBoost algorithm with four inputs produced the most precise predictions with the highest R2 value of 0.941, the lowest RMSE value of 4.455, and MAE of 2.861 for the overall databank. Based on SHAP values, the substrates are the most impactful variables of the XGBoost model. The predicted hydrogen column height was also evaluated for a specific storage site in Australia's basalt formation. At 308 K, the predicted hydrogen column height decreased from 1991 to 1319 m, while at 343 K, it decreased from 1510 to 784 m. These predictions were compared to the real column height that fell from 1660 to 928 m in the same pressure range. Overall, the study's findings provide a valuable guide for predicting wettability and evaluating hydrogen column height in specific storage sites. The use of machine learning algorithms can significantly reduce the time, cost, and unpredictability associated with traditional methods of assessing hydrogen wettability in underground storage sites.

    DOI

    Scopus

    9
    被引用数
    (Scopus)
  • Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations[Formula presented]

    Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, Valeriy S. Rukavishnikov

    Applied Soft Computing   143  2023年08月

     概要を見る

    Emissions of carbon dioxide (CO2) are a major source of atmospheric pollution contributing to global warming. Carbon geological sequestration (CGS) in saline aquifers offers a feasible solution to reduce the atmospheric buildup of CO2. The direct determination of the trapping efficiency of CO2 in potential storage formations requires extensive, time-consuming simulations. Machine-learning (ML) models offer a complementary means of determining trapping indexes, thereby reducing the number of simulations required. However, ML models have to date found it difficult to accurately predict two specific reservoir CO2 indexes: residual-trapping index (RTI) and solubility-trapping index (STI). Hybridizing ML models with optimizers (HML) demonstrate better RTI and STI prediction performance by selecting the ML model's hyperparameters more precisely. This study develops and evaluates six HML models, combining a least-squares-support-vector machine (LSSVM) and a radial-basis-function neural network (RBFNN) with three effective optimizer algorithms: genetic (GA), cuckoo optimization (COA), and particle-swarm optimization (PSO). 6810 geological-formation simulation records for RTI and STI were compiled from published studies and evaluated with the six HML models. Error and score analysis reveal that the HML models outperform standalone ML models in predicting RTI and STI for this dataset, with the LSSVM-COA model achieving the lowest root mean squared errors of 0.00421 and 0.00067 for RTI and STI, respectively. Sensitivity analysis identifies residual gas saturation and permeability as the most influential input variables on STI and RTI predictions. The high RTI and STI prediction accuracy achieved by the HML models offers to reduce the uncertainties associated with CGS projects substantially.

    DOI

    Scopus

    13
    被引用数
    (Scopus)
  • Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

    Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, Valeriy S. Rukavishnikov, Zhenxue Dai

    Expert Systems with Applications   222  2023年07月

     概要を見る

    Ongoing anthropogenic carbon dioxide (CO2) emissions to the atmosphere cause severe air pollution that leads to complex changes in the climate, which pose threats to human life and ecosystems more generally. Geological CO2 storage (GCS) offers a promising solution to overcome this critical environmental issue by removing some of the CO2 emissions. The performance of GCS projects depends directly on the solubility and residual trapping efficiency of CO2 in a saline aquifer. This study models the solubility trapping index (STI) and residual trapping index (RTI) of CO2 in saline aquifers by applying four robust machine learning (ML) and deep learning (DL) algorithms. Extreme learning machine (ELM), least square support vector machine (LSSVM), general regression neural network (GRNN), and convolutional neural network (CNN) are applied to 6811 compiled simulation records from published studies to provide accurate STI and RTI predictions. Employing different statistical error metrics coupled with supplementary evaluations, involving score and robustness analyses, the prediction accuracy of the models proposed is comparatively assessed. The findings of the study revealed that the LSSVM model delivers the lowest RMSE values: 0.0043 (STI) and 0.0105 (RTI) with few outlying predictions. Presenting the highest STI and RTI prediction scores the LSSVM is distinguished as the most credible model among all the four models studied. The models consider eight input variables, of which the time elapsed and injection rate displays the strongest correlations with STI and RTI, respectively. The results suggest that the proposed LSSVM model is best suited for monitoring CO2 sequestration efficiency from the data variables considered. Applying such models avoids time-consuming complex simulations and offers the potential to generate fast and reliable assessments of GCS project feasibility. Accurate modeling of CO2 storage trapping indexes guarantees successful geological CO2 storage operation, which is, in fact, the cornerstone of properly controlling and managing environmentally polluting gases.

    DOI

    Scopus

    19
    被引用数
    (Scopus)
  • Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

    Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, Mohammed Al-Shargabi, Valeriy S. Rukavishnikov

    Separation and Purification Technology   316  2023年07月

     概要を見る

    Hydrogen (H2) absorption percentage by porous carbon media (PCM) is important for identifying efficient H2 storage media. PCM with H2-uptakes of greater than 5 wt% are urgently required to improve the performance of H2 fuel tanks for use in fuel-cell-powered transportation vehicles. Machine-learning (ML) methods can provide effective tools for predicting PCM H2-uptakes from influential variables determined by experiments performed on a wide range of PCM. This study evaluates the PCM-H2-uptake prediction performance of four well-established ML models: generalized-regression neural network (GRNN), Least-squares-support-vector machine (LSSVM), adaptive-neuro-fuzzy-inference system (ANFIS), and extreme-learning machine (ELM). A 2072-record database, compiled from literature, comprising eleven independent variables and PCM H2-uptake (dependent variable covering a range of 0 to 8.38 wt%) was evaluated by the four ML models. Each model was trained and validated using 10-fold cross-validation. The LSSVM generates the best PCM-H2-uptake prediction performance when applied to an independent testing subset of data records, achieving a root mean squared error of just 0.2407 wt%. Feature importance sensitivity analysis identifies pressure as the most influential of the independent variable considered. Leverage analysis identified that 96.53% of the data records of the compiled database, when predicted by the LSSVM model, resided within the applicable domain with only seventy-two data records considered as suspected outliers. These results indicate that the LSSVM model developed is highly generalizable for the purpose of predicting PCM H2-uptake from the influential variables.

    DOI

    Scopus

    11
    被引用数
    (Scopus)
  • Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage

    Hemeng Zhang, Hung Vo Thanh, Mohammad Rahimi, Watheq J. Al-Mudhafar, Suparit Tangparitkul, Tao Zhang, Zhenxue Dai, Umar Ashraf

    Science of the Total Environment   877  2023年06月

     概要を見る

    The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero. The data were collected from 229 datasets of contact angle in three states of shale/oil/brine, shale/CO2/brine, and shale/CH4/brine systems. Five algorithms were used to tune MLP, while three optimization algorithms were used to optimize the RBFNN computing framework. The results indicate that the RBFNN-MVO model achieved the best predictive accuracy, with a root mean square error (RMSE) value of 0.113 and an R2 of 0.999993. The sensitivity analysis showed that theta zero, TOC, pressure, temperature, and salinity were the most sensitive features. This research demonstrates the effectiveness of RBFNN-MVO model in evaluating shale wettability for CCUS initiatives and cleaner production.

    DOI PubMed

    Scopus

    14
    被引用数
    (Scopus)
  • Upscaling dispersivity for conservative solute transport in naturally fractured media

    Sida Jia, Zhenxue Dai, Zhichao Zhou, Hui Ling, Zhijie Yang, Linlin Qi, Zihao Wang, Xiaoying Zhang, Hung Vo Thanh, Mohamad Reza Soltanian

    Water Research   235  2023年05月

     概要を見る

    Physical heterogeneities are prevalent features of fracture systems and significantly impact transport processes in aquifers across different spatiotemporal scales. Upscaling solute transport parameter is an effective way of quantifying parameter variability in heterogeneous aquifers including fractured media. This paper develops conceptual models for upscaling conservative transport parameters in fracture media. The focus is on upscaling dispersivity. Lagrangian-based transport model (LBTM) for dispersivity upscaling are derived for the solute transport in two-dimensional fractures surrounded by an impermeable matrix. The LBTM is validated against the random walk particle tracking (RWPT) model, which enables highly efficient and accurate predictions of conservative solute transport. The results show that the derived scale-dependent analytical expressions are in excellent agreement with RWPT model results. In addition, LBTM results are also compared to experimental results from the observed breakthrough curve of a conservative solute transport through a single natural fracture within a granite core. Comparing results from the LBTM and transport experiment shows that LBTM based estimated dispersivity is 10.55% higher than the measured value. Errors introduced by the experiments, the conceptual assumptions in deriving models, and the heterogeneities of fracture apertures not fully sampled by measuring instruments are main factor for such discrepancy. The sensitivity analysis indicates that the longitudinal and transverse dispersivities are positively related to the integral scale and the variance of the log-fracture aperture. The longitudinal dispersivity is strongly contolled by the variance of the log-fracture aperture. The LBTM may be useful for directly predicting solute transports, requiring only the acquisition of fractured geostatistical data. This work provides a better understanding of transport processes in fractured media which ultimately control water quality across scales.

    DOI PubMed

    Scopus

    21
    被引用数
    (Scopus)
  • Depositional and diagenetic modeling of the Margala Hill Limestone, Hazara area (Pakistan): Implications for reservoir characterization using outcrop analogues

    Shuja Ullah, Muhammad Hanif, Ahmed E. Radwan, Chuanxiu Luo, Nazir Ur Rehman, Sajjad Ahmad, Khalid Latif, Nowrad Ali, Hung Vo Thanh, Muhammad Asim, Umar Ashraf

    Geoenergy Science and Engineering    2023年05月

    DOI

    Scopus

    5
    被引用数
    (Scopus)
  • Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management

    Songlin Yang, Huiqing Lian, Bin Xu, Hung Vo Thanh, Wei Chen, Huichao Yin, Zhenxue Dai

    Science of the Total Environment   871  2023年05月

     概要を見る

    Traditional mine water inflow prediction is characterized by a high degree of uncertainty in model parameters and complex mechanisms involved in the water inflow process. Data-driven models play a key role in predicting inflow mechanisms without considering physical changes. However, the existing models are limited by nonlinearity and non-stationarity. Thus, the principal objective of this study was to propose two robust models, the DIFF-TCN model and the DIFF-LSTM model, for predicting the average water inflow per day. The models consist of three methods, namely Difference Method (DIFF), Temporal Convolutional Neural Network (TCN), and Long Short-Term Memory Neural Network (LSTM). When applied to the Tingnan Coal Mine, Shanxi Province, China, the DIFF-TCN performs better in predicting the average daily water inflow, the model has a MAE of 5.88 m3/h, RMSE of 6.85 m3/h and R2 of 0.96 in the test stage of the water inflow event. Comparison with the other deep learning models (with similar complex structures) and traditional time series model shows the superiority of our proposed DIFF-TCN model. The SHAP value is used to explain the contribution of each model input to the predicted values, and it indicates that the historical time of water inflow data are the most important input, and the advance distance and the groundwater level data also contribute to the model predictions, but groundwater level data for some periods in the past may have a detrimental effect on the model. The findings of this study can provide better understanding about potential of robust deep learning models for smart hydrological forecasting, and they can also provide technical guidance for mining safety production and protection of water resources and water environment around the mining area.

    DOI PubMed

    Scopus

    10
    被引用数
    (Scopus)
  • A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors

    Mohammad Rahimi, Hung Vo Thanh, Iman Ebrahimzade, Mohammad Hossein Abbaspour-Fard, Abbas Rohani

    Journal of Cleaner Production   397  2023年04月

     概要を見る

    The selection of desirable synthesis procedures to achieve the idea of physiochemical and capacitive properties of activated carbons (ACs) can be carried out by the multi-criteria decision-making (MCDM) technique. The technique of ordering preference by similarity to an ideal solution (TOPSIS) is a well-known MCDM method with a superior selection of ideal materials. The present work elaborates a framework by establishing the TOPSIS method to obtain ideal synthesizing features, materials, and electrochemical measurements of AC electrode. The 21 multi-alternatives are considered from (i) temperature/heating rate of carbonization, chemical activating/doping, and post-purifying procedures of ACs; and (ii) the ratios of components, electrolytes, and potential window from AC-derived electrodes. 12 creteria are obtained from categories include microstructure properties, heteroatoms content of ACs, and gravimetric capacitance of AC electrodes used in electric double-layer capacitors (EDLCs). TOPSIS proposed scores for activating agent ratio, activation temperature, and heating rate of 0.68, 0.65, and 0.57 on the physiochemical criteria of AC, respectively. Also, the electrolyte concentration, type, and ratio of activating agents were ranked with 1, 082, and 062 scores on electrode capacitance, respectively. Moreover, TOPSIS exemplified two-step hydrothermal-assisted synthesis, artificial doping, and 6M HCl for purifying to achieve ACs with ideal physiochemical and capacitive performance. The MCDM technique proved that it can be applied to select the ideal material and process in AC-electrode fabrications with less time, financial, and environmental costs.

    DOI

    Scopus

    4
    被引用数
    (Scopus)
  • Quantitative Characterization of Shallow Marine Sediments in Tight Gas Fields of Middle Indus Basin: A Rational Approach of Multiple Rock Physics Diagnostic Models

    Muhammad Ali, Umar Ashraf, Peimin Zhu, Huolin Ma, Ren Jiang, Guo Lei, Jar Ullah, Jawad Ali, Hung Vo Thanh, Aqsa Anees

    Processes   11 ( 2 )  2023年02月

     概要を見る

    For the successful discovery and development of tight sand gas reserves, it is necessary to locate sand with certain features. These features must largely include a significant accumulation of hydrocarbons, rock physics models, and mechanical properties. However, the effective representation of such reservoir properties using applicable parameters is challenging due to the complicated heterogeneous structural characteristics of hydrocarbon sand. Rock physics modeling of sandstone reservoirs from the Lower Goru Basin gas fields represents the link between reservoir parameters and seismic properties. Rock physics diagnostic models have been utilized to describe the reservoir sands of two wells inside this Middle Indus Basin, including contact cement, constant cement, and friable sand. The results showed that sorting the grain and coating cement on the grain’s surface both affected the cementation process. According to the models, the cementation levels in the reservoir sands of the two wells ranged from 2% to more than 6%. The rock physics models established in the study would improve the understanding of characteristics for the relatively high Vp/Vs unconsolidated reservoir sands under study. Integrating rock physics models would improve the prediction of reservoir properties from the elastic properties estimated from seismic data. The velocity–porosity and elastic moduli-porosity patterns for the reservoir zones of the two wells are distinct. To generate a rock physics template (RPT) for the Lower Goru sand from the Early Cretaceous period, an approach based on fluid replacement modeling has been chosen. The ratio of P-wave velocity to S-wave velocity (Vp/Vs) and the P-impedance template can detect cap shale, brine sand, and gas-saturated sand with varying water saturation and porosity from wells in the Rehmat and Miano gas fields, both of which have the same shallow marine depositional characteristics. Conventional neutron-density cross-plot analysis matches up quite well with this RPT’s expected detection of water and gas sands.

    DOI

    Scopus

    4
    被引用数
    (Scopus)
  • Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

    Hung Vo Thanh

    Environmental Science and Pollution Research    2023年

    DOI

  • 19
    被引用数
    (Scopus)
  • Correction: 3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam

    Hung Vo Thanh, Kang-Kun Lee

    Journal of Petroleum Exploration and Production Technology    2023年01月

    DOI

    Scopus

  • Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

    Mohammed A. A. Al-qaness, Ahmed A. Ewees, Laith Abualigah, Ayman Mutahar AlRassas, Hung Vo Thanh, mohamed abd el aziz

    Entropy    2022年11月

    DOI

    Scopus

    17
    被引用数
    (Scopus)
  • Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals

    Hung Vo Thanh, Majid Safaei-Farouji, Ning Wei, Shahab S. Band, Amir Mosavi

    Energy Reports    2022年11月

    DOI

    Scopus

    7
    被引用数
    (Scopus)
  • Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites

    Hung Vo Thanh

    Renewable Energy   200   169 - 184  2022年11月  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

  • Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project

    Majid Safaei-Farouji, Hung Vo Thanh, Zhenxue Dai, Abolfazl Mehbodniya, Mohammad Rahimi, Umar Ashraf, Ahmed E. Radwan

    Journal of Cleaner Production    2022年10月

    DOI

    Scopus

    36
    被引用数
    (Scopus)
  • Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

    Watheq J. Al‐Mudhafar, Dandina N. Rao, Sanjay Srinivasan, Hung Vo Thanh, Erfan M. Al Lawe

    Energy Science & Engineering    2022年10月

    DOI

    Scopus

    7
    被引用数
    (Scopus)
  • Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan

    Nafees Ali, Xiaodong Fu, Umar Ashraf, Jian Chen, Hung Vo Thanh, Aqsa Anees, Muhammad Shahid Riaz, Misbah Fida, Muhammad Afaq Hussain, Sadam Hussain, Wakeel Hussain, Awais Ahmed

    Sustainability    2022年08月

    DOI

    Scopus

    6
    被引用数
    (Scopus)
  • Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan

    Nafees Ali, Xiaodong Fu, U. Ashraf, Jian Chen, Hung Vo Thanh, A. Anees, Muhammad Shahid Riaz, Misbah Fida, Muhammad Afaq Hussain, Sadam Hussain, Wakeel Hussain, Awais Ahmed

    Sustainability    2022年08月

    DOI

    Scopus

    6
    被引用数
    (Scopus)
  • Developing the efficiency-modeling framework to explore the potential of CO2 storage capacity of S3 reservoir, Tahe oilfield, China

    Ahmed Alalimi, Ayman Mutahar AlRassas, Hung Vo Thanh, Mohammed A. A. Al-qaness, Lin Pan, Umar Ashraf, Dalal AL-Alimi, Safea Moharam

    Geomechanics and Geophysics for Geo-Energy and Geo-Resources    2022年08月

    DOI

    Scopus

    17
    被引用数
    (Scopus)
  • Prediction of Cretaceous reservoir zone through petrophysical modeling: Insights from Kadanwari gas field, Middle Indus Basin

    Nafees Ali, Jian Chen, Xiaodong Fu, Wakeel Hussain, Muhammad Ali, Mazahir Hussain, Aqsa Anees, Muhammad Rashid, Hung Vo Thanh

    Geosystems and Geoenvironment    2022年08月

    DOI

    Scopus

    13
    被引用数
    (Scopus)
  • Paleoenvironmental and Bio-Sequence Stratigraphic Analysis of the Cretaceous Pelagic Carbonates of Eastern Tethys, Sulaiman Range, Pakistan

    Shuja Ullah, Irfan U. Jan, Muhammad Hanif, Khalid Latif, Mohibullah Mohibullah, Mahnoor Sabba, Aqsa Anees, Umar Ashraf, Hung Vo Thanh

    Minerals    2022年07月

    DOI

    Scopus

    10
    被引用数
    (Scopus)
  • Paleoenvironmental and Bio-Sequence Stratigraphic Analysis of the Cretaceous Pelagic Carbonates of Eastern Tethys, Sulaiman Range, Pakistan

    Shuja Ullah, Irfan U. Jan, Muhammad Hanif, Khalid Latif, Mohibullah Mohibullah, Mahnoor Sabba, A. Anees, U. Ashraf, Hung Vo Thanh

    Minerals    2022年07月

    DOI

    Scopus

    10
    被引用数
    (Scopus)
  • Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

    Hung Vo Thanh

    Water Resources Research   58 ( 5 )  2022年05月  [査読有り]

    担当区分:筆頭著者

    DOI

    Scopus

    13
    被引用数
    (Scopus)
  • Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers

    Hung Vo Thanh

    Applied Energy   314  2022年05月  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

  • Experimental investigation on plugging performance of CO2 microbubbles in porous media

    Hung Vo Thanh

    Journal of Petroleum Science and Engineering    2022年04月

    DOI

  • Integrated static modeling and dynamic simulation framework for CO2 storage capacity in Upper Qishn Clastics, S1A reservoir, Yemen

    Ayman Mutahar AlRassas, Hung Vo Thanh, Shaoran Ren, Renyuan Sun, Nam Le Nguyen Hai, Kang-Kun Lee

    Geomechanics and Geophysics for Geo-Energy and Geo-Resources   8 ( 1 )  2022年02月

    DOI

    Scopus

    9
    被引用数
    (Scopus)
  • Fault and fracture network characterization using seismic data: a study based on neural network models assessment

    Hung Vo Thanh

    Geomechanics and Geophysics for Geo-energy and Geo-Resources    2022年

    DOI

  • Integrated static modeling and dynamic simulation framework for CO2 storage capacity in Upper Qishn Clastics, S1A reservoir, Yemen

    Hung Vo Thanh

    Geomechanics and Geophysics for Geo-energy and Geo-Resources    2022年

    DOI

  • Application of machine learning to predict CO2 trapping performance in deep saline aquifers

    Hung Vo Thanh, Kang-Kun Lee

    Energy    2022年01月

    DOI

    Scopus

    46
    被引用数
    (Scopus)
  • Developing the efficiency-modeling framework to explore the potential of CO2 storage capacity of S3 reservoir, Tahe oilfield, China

    Hung Vo Thanh

    Geomechanics and Geophysics for Geo-energy and Geo-Resources    2022年

    DOI

  • 3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam

    Hung Vo Thanh

    Journal of Petroleum Exploration and Production Technology    2022年01月

    DOI

  • Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

    Hung Vo Thanh

    Scientific Reports   10 ( 1 ) 1 - 16  2020年10月  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

  • Impact of a new geological modelling method on the enhancement of the CO<inf>2</inf> storage assessment of E sequence of Nam Vang field, offshore Vietnam

    Hung Vo Thanh

    Energy Sources, Part A: Recovery, Utilization and Environmental Effects   42 ( 12 ) 1499 - 1512  2020年06月  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

    Scopus

    54
    被引用数
    (Scopus)
  • Robust optimization of CO<inf>2</inf> sequestration through a water alternating gas process under geological uncertainties in Cuu Long Basin, Vietnam

    Hung Vo Thanh

    Journal of Natural Gas Science and Engineering   76   103208 - 103208  2020年04月  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

    Scopus

    46
    被引用数
    (Scopus)
  • Microbe-induced fluid viscosity variation: field-scale simulation, sensitivity and geological uncertainty

    Hung Vo Thanh

    Journal of Petroleum Exploration and Production Technology    2020年02月

    DOI

    Scopus

    9
    被引用数
    (Scopus)
  • Applying the hydrodynamic model to optimize the production for crystalline basement reservoir, X field, Cuu Long Basin, Vietnam

    Hung Vo Thanh

    Journal of Petroleum Exploration and Production Technology    2020年

    DOI

  • Microbe-induced fluid viscosity variation: field-scale simulation, sensitivity and geological uncertainty

    Hung Vo Thanh

    Journal of Petroleum Exploration and Production Technology    2020年

    DOI

  • Microbe-induced fluid viscosity variation: field-scale simulation, sensitivity and geological uncertainty

    Hung Vo Thanh

    Journal of Petroleum Exploration and Production Technology    2020年

    DOI

    Scopus

    9
    被引用数
    (Scopus)
  • 7
    被引用数
    (Scopus)
  • Applying the hydrodynamic model to optimize the production for crystalline basement reservoir, X field, Cuu Long Basin, Vietnam

    Ngoc Thai Ba, Hung Vo Thanh, Yuichi Sugai, Kyuro Sasaki, Ronald Nguele, Trung Phi Hoang Quang, Minh Luong Bao, Nam Le Nguyen Hai

    Journal of Petroleum Exploration and Production Technology   10 ( 1 ) 31 - 46  2020年01月

    DOI

    Scopus

    7
    被引用数
    (Scopus)
  • Integrated workflow in 3D geological model construction for evaluation of CO<inf>2 storage capacity of a fractured basement r</inf>eservoir in Cuu Long Basin, Vietnam

    Hung Vo Thanh

    International Journal of Greenhouse Gas Control   90   102826 - 102826  2019年11月  [査読有り]

    担当区分:筆頭著者, 責任著者

    DOI

    Scopus

    88
    被引用数
    (Scopus)

▼全件表示

Works(作品等)

  • Petroleum Engineer

    ConocoPhillips, Oil States  その他 

    2013年05月
    -
    2015年05月

講演・口頭発表等

  • Coupling Genetic algorithm and Random Forest for robust prediction of CO2 storage efficiency in underground formations

    84th EAGE Annual Conference & Exhibition  

    発表年月: 2023年06月

    開催年月:
    2023年06月
     
     
  • Integrated artificial neural network and object-based modelling for enhancement history matching in a fluvial channel sandstone reservoir

    Hung Vo Thanh

    SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition  

    発表年月: 2019年10月

    開催年月:
    2019年10月
     
     

共同研究・競争的資金等の研究課題

  • CO2貯留効率向上技術の開発

    研究期間:

    2020年12月
    -
    2022年05月
     

Misc

  • Subsurface sedimentary structure identification using deep learning: A review

    Chuanjun Zhan, Zhenxue Dai, Zhijie Yang, Xiaoying Zhang, Ziqi Ma, Hung Vo Thanh, Mohamad Reza Soltanian

    Earth-Science Reviews   239  2023年04月

    書評論文,書評,文献紹介等  

     概要を見る

    The reliable identification of subsurface sedimentary structures (i.e., geologic heterogeneity) is critical in various earth and environmental sciences, petroleum reservoir engineering, and other porous media-related application. The application includes some important and societally relevant problems such as contaminated aquifer remediation, enhanced oil recovery, geological carbon storage, geological hydrogen storage, radioactive waste disposal, and contaminant fate and transport modeling. An inaccurately estimated subsurface sedimentary structure may introduce a larger bias into simulation results than inappropriate model parameters. Research on the development of subsurface sedimentary structure identification methods has recently witnessed increasing interest in deep learning (DL)-based methods. Such methods allow structure identification in a considerably different manner compared to traditional methods (e.g., covariance-based (co)kriging, multi-point statistics). The DL-based methods achieve significantly higher efficiency and accuracy. This review describes how DL-based methods have been utilized for subsurface sedimentary structure identification from the viewpoint of different identification approaches (direct and data assimilation-based modeling). Differences between DL-based and traditional methods are discussed. Furthermore, the limitations and challenges of existing DL-based methods are summarized. This includes training data acquisition, comparison of different algorithms, and limitations on accuracy and efficiency. Finally, future research directions are explored, including coupling DL-based and traditional methods, development of benchmark databases, DL-based methods driven by both data and theory, and applications of meta- and transfer learning. Effective solutions to these problems can provide numerous opportunities for DL-based methods to realize advances in subsurface sedimentary structure identification, thereby enabling a deeper scientific understanding of subsurface sedimentary structures.

    DOI

 

現在担当している科目

 

社会貢献活動

  • Online Seminar with Vietnam Universities

    2022年12月
    -
    継続中

学術貢献活動

  • Reviewer for Fuel, Applied Energy, Journal of CO2 Utilization, SPE Journal, Bioresources Technology, Scientific Reports (Nature), International Greenhouse Gas Control, Energy

    審査・学術的助言

    2020年12月
    -
    継続中