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

Scientific reports, 2020-10, Vol.10 (1), p.18204-18204, Article 18204

2020

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Integrated workflow in 3D geological model construction for evaluation of CO2 storage capacity of a fractured basement reservoir in Cuu Long Basin, Vietnam

International journal of greenhouse gas control, 2019-11, Vol.90, p.102826, Article 102826

2019

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Pyramid with Super Resolution for In-The-Wild Facial Expression Recognition

IEEE access, 2020-01, Vol.8, p.1-1

2020

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Experimental study of moisture effects on spontaneous combustion of Baiyinhua lignite from individual particles to stockpile

Fuel (Guildford), 2023-02, Vol.334, p.126774, Article 126774

2023

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Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis

Minerals (Basel), 2023-01, Vol.13 (1), p.29

2023

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

Energy (Oxford), 2022-01, Vol.239, p.122457, Article 122457

2022

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Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers

Applied energy, 2022-05, Vol.314, p.118985, Article 118985

2022

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Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Frontiers in earth science (Lausanne), 2022-09, Vol.10

2022

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Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects

Energy (Oxford), 2023-09, Vol.278, p.127860, Article 127860

2023

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Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam

Energy sources. Part A, Recovery, utilization, and environmental effects, 2020-06, Vol.42 (12), p.1499-1512

2020

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Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme

Fuel (Guildford), 2023-08, Vol.345, p.128183, Article 128183

2023

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Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

Expert systems with applications, 2023-07, Vol.222, p.119796, Article 119796

2023

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

Journal of cleaner production, 2022-10, Vol.372, p.133778, Article 133778

2022

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Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites

Renewable energy, 2022-11, Vol.200, p.169-184

2022

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A Deep Learning-Based Data-Driven Approach for Predicting Mining Water Inrush From Coal Seam Floor Using Microseismic Monitoring Data

IEEE transactions on geoscience and remote sensing, 2023, Vol.61, p.1-15

2023

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Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Energy science & engineering, 2022-10, Vol.10 (10), p.4112-4135

2022

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

International journal of hydrogen energy, 2024-02, Vol.55, p.1422-1433

2024

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Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations

Applied soft computing, 2023-08, Vol.143, p.110408, Article 110408

2023

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Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

Separation and purification technology, 2023-07, Vol.316, p.123807, Article 123807

2023

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Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

International journal of hydrogen energy, 2024-09, Vol.83, p.1-12

2024

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Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder

Electronics (Basel), 2021-06, Vol.10 (12), p.1396

2021

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Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Energy & fuels, 2024-03, Vol.38 (5), p.3635-3644

2024

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Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping

Fuel (Guildford), 2022-07, Vol.319, p.123821, Article 123821

2022

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Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study

Expert systems with applications, 2024-09, Vol.250, p.123944, Article 123944

2024

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Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models

Journal of cleaner production, 2024-02, Vol.441, p.141043, Article 141043

2024

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

Integrated workflow in 3D geological model construction for evaluation of CO2 storage capacity of a fractured basement reservoir in Cuu Long Basin, Vietnam

Pyramid with Super Resolution for In-The-Wild Facial Expression Recognition

Experimental study of moisture effects on spontaneous combustion of Baiyinhua lignite from individual particles to stockpile

Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis

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

Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects

Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam

Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme

Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

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

Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites

A Deep Learning-Based Data-Driven Approach for Predicting Mining Water Inrush From Coal Seam Floor Using Microseismic Monitoring Data

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

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

Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations

Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping

Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study

Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models

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