Modeling of CO
            <sub>2</sub>
            storage as hydrate in vacated natural gas hydrate formation

Ahmad, Sheraz; Li, Yiming; Li, Xiangfang; Xia, Wei; Chen, Zeen; Wang, Peng

doi:10.1002/er.4968

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…With regard to these two concerns, CO 2 sequestration in the marine sediments in the deep ocean in the form of gas hydrates may be a reliable method. 436 Theoretically, CO 2 hydrates will not decompose and can be stably stored in deep sea areas where the hydrostatic pressure is above the phase equilibrium of CO 2 hydrates, thereby avoiding gas leakage and ocean acidification. In addition, a low-permeability secondary cap can form to prevent CO 2 diffusion when forming gas hydrates in marine sediments.…”

Section: Outlooks For the Role Of Natural Gas Hydrates And Their Rela...mentioning

confidence: 99%

Natural gas hydrate resources and hydrate technologies: a review and analysis of the associated energy and global warming challenges

Zhang

Jian-wu

et al. 2021

Energy Environ. Sci.

229

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Section: Outlooks For the Role Of Natural Gas Hydrates And Their Rela...mentioning

confidence: 99%

Natural gas hydrate resources and hydrate technologies: a review and analysis of the associated energy and global warming challenges

Zhang

Jian-wu

et al. 2021

Energy Environ. Sci.

229

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Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study

Aleem,

Ahmad,

Qamar

et al. 2024

Arab J Sci Eng

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Accurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R2, ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R2 score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

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Review of non-isothermal processes in CCUS from a geomechanical perspective

Lu,

Yu,

Zhang

et al. 2024

Earth-Science Reviews

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Modeling of CO ₂ storage as hydrate in vacated natural gas hydrate formation

Cited by 3 publications

References 59 publications

Natural gas hydrate resources and hydrate technologies: a review and analysis of the associated energy and global warming challenges

Natural gas hydrate resources and hydrate technologies: a review and analysis of the associated energy and global warming challenges

Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study

Review of non-isothermal processes in CCUS from a geomechanical perspective

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Modeling of CO 2 storage as hydrate in vacated natural gas hydrate formation

Cited by 3 publications

References 59 publications

Natural gas hydrate resources and hydrate technologies: a review and analysis of the associated energy and global warming challenges

Natural gas hydrate resources and hydrate technologies: a review and analysis of the associated energy and global warming challenges

Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study

Review of non-isothermal processes in CCUS from a geomechanical perspective

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Modeling of CO ₂ storage as hydrate in vacated natural gas hydrate formation