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Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns

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  • Ali, Aliyuda

Abstract

This paper proposes a collection of novel deliverability prediction models for underground natural gas storage (UNGS) in salt caverns based on machine learning algorithms. Considering that the natural gas supply chain is characterized by imbalances between demand and supply on a timely basis, effective and fast models for predicting the deliverability of UNGS would not only be a valuable tool to various stakeholders but also, of great benefit in competitive natural gas marketplace. In this paper, a first step in applying machine learning algorithms to predict the deliverability of UNGS in salt caverns is proposed. To achieve this, the capability of three machine learning algorithms namely, artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF) are examined. The predictive capabilities of these methods were investigated using different monthly field storage data samples for different years with varied data samples of 36 active UNGS in salt caverns in the United States. Experimental results reveal that the machine learning models developed in this study can serve as suitable tools for predicting the deliverability of UNGS in salt caverns with different performances. Overall result shows that RF model exhibits better prediction performance with varied data partitions over ANN and SVM models.

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  • Ali, Aliyuda, 2021. "Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221008975
    DOI: 10.1016/j.energy.2021.120648
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    2. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
    3. Qiao, Weibiao & Fu, Zonghua & Du, Mingjun & Nan, Wei & Liu, Enbin, 2023. "Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow searc," Energy, Elsevier, vol. 274(C).
    4. Vo Thanh, Hung & Yasin, Qamar & Al-Mudhafar, Watheq J. & Lee, Kang-Kun, 2022. "Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers," Applied Energy, Elsevier, vol. 314(C).
    5. Du, Zhengyang & Dai, Zhenxue & Yang, Zhijie & Zhan, Chuanjun & Chen, Wei & Cao, Mingxu & Thanh, Hung Vo & Soltanian, Mohamad Reza, 2024. "Exploring hydrogen geologic storage in China for future energy: Opportunities and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    6. Vo Thanh, Hung & Zamanyad, Aiyoub & Safaei-Farouji, Majid & Ashraf, Umar & Hemeng, Zhang, 2022. "Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites," Renewable Energy, Elsevier, vol. 200(C), pages 169-184.
    7. Zhang, Xiong & Liu, Wei & Jiang, Deyi & Qiao, Weibiao & Liu, Enbin & Zhang, Nan & Fan, Jinyang, 2021. "Investigation on the influences of interlayer contents on stability and usability of energy storage caverns in bedded rock salt," Energy, Elsevier, vol. 231(C).
    8. Liu, Shuhan & Sun, Wenqiang, 2023. "Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation," Energy, Elsevier, vol. 262(PA).
    9. Lu, Yutian & Wang, Bo & Zhao, Yingying & Yang, Xiaochen & Li, Lizhe & Dong, Mingzhi & Lv, Qin & Zhou, Fujian & Gu, Ning & Shang, Li, 2022. "Physics-informed surrogate modeling for hydro-fracture geometry prediction based on deep learning," Energy, Elsevier, vol. 253(C).

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