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

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  • Vo Thanh, Hung
  • Zamanyad, Aiyoub
  • Safaei-Farouji, Majid
  • Ashraf, Umar
  • Hemeng, Zhang

Abstract

Underground natural gas storage is a promising solution to lowering greenhouse gas emissions and attaining sustainable development goals. However, several issues prevent the application of storage projects on a global scale. An accurate estimation of the delivered amount of natural gas from each storage site might be used for supply and demand. Due to this fact, this study proposed hybrid intelligent models integrating the least square support vector machine (LSSVM), differential evolution (DE), imperialist competitive algorithm (ICA), cultural algorithm (CA), teaching learning-based optimization (TLBO), genetic algorithm (GA), and particle swarm optimization (PSO) for approximating the deliverability of underground natural gas storage in different geological formations. We have employed vast data sets of 782 reservoirs from depleted fields to train and validate the proposed intelligent models to predict underground natural gas storage deliverability in the USA. The visual and analytical assessments were used to investigate the performance of the developed intelligent systems. The predicted results showed that all of the intelligent models agreed with the recorded data. Moreover, the statistical indicators revealed that the LSSVM coupling TLBO model shows the highest accuracy in predicting the deliverability of natural gas storage in the depleted field among three intelligent models. Also, the optimal intelligent model accurately predicts 880 and 600 data measurements of saline aquifers and salt domes, respectively. The optimal intelligent model yields a root mean square error (RMSE) value of less than 0.022. The correlation factor (R2) is over 0.998, 0.999, and 0.906 for the depleted field, saline aquifers, and salt domes, respectively. The results highlight the importance of combining smart approaches with nature-inspired strategies in forecasting storage site deliverability. In light of these findings, researchers are better equipped to reduce petroleum energy usage and increase community acceptability of natural gas as part of the transition to green energy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:200:y:2022:i:c:p:169-184
    DOI: 10.1016/j.renene.2022.09.132
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    References listed on IDEAS

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    1. Liu, Bo & Mohammadi, Mohammad-Reza & Ma, Zhongliang & Bai, Longhui & Wang, Liu & Wen, Zhigang & Liu, Yan & Morta, Hem Bahadur & Hemmati-Sarapardeh, Abdolhossein & Ostadhassan, Mehdi, 2023. "Experimental investigation and intelligent modeling of pore structure changes in type III kerogen-rich shale artificially matured by hydrous and anhydrous pyrolysis," Energy, Elsevier, vol. 282(C).
    2. Long, Keji & Tang, Yong & He, Youwei & Luo, Yulong & Hong, Yinghe & Sun, Yu & Rui, Zhenhua, 2024. "Full-cycle enhancing condensate recovery-underground gas storage by integrating cyclic gas flooding and storage from gas condensate reservoirs," Energy, Elsevier, vol. 293(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 & Sheini Dashtgoli, Danial & Zhang, Hemeng & Min, Baehyun, 2023. "Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects," Energy, Elsevier, vol. 278(PA).

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