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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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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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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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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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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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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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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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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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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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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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CO2 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

Energy & fuels, 2022-09, Vol.36 (17), p.10299-10314

2022

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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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Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

Environmental science and pollution research international, 2023-03, Vol.30 (12), p.33780-33794

2023

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Robust optimization of CO2 sequestration through a water alternating gas process under geological uncertainties in Cuu Long Basin, Vietnam

Journal of natural gas science and engineering, 2020-04, Vol.76, p.103208, Article 103208

2020

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Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems

Journal of environmental chemical engineering, 2024-10, Vol.12 (5), p.113435, Article 113435

2024

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Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

Scientific reports, 2024-01, Vol.14 (1), p.22-22, Article 22

2024

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Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model

Energies (Basel), 2023-03, Vol.16 (5), p.2348

2023

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Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Journal of environmental chemical engineering, 2024-04, Vol.12 (2), p.112210, Article 112210

2024

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Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals

Energy reports, 2022-11, Vol.8, p.7643-7656

2022

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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

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

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

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

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

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

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

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

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

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

CO2 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

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

Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

Robust optimization of CO2 sequestration through a water alternating gas process under geological uncertainties in Cuu Long Basin, Vietnam

Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems

Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model

Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals

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