IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v280y2023ics0360544223016493.html
   My bibliography  Save this article

Comparative data-driven enhanced geothermal systems forecasting models: A case study of Qiabuqia field in China

Author

Listed:
  • Xue, Zhenqian
  • Zhang, Kai
  • Zhang, Chi
  • Ma, Haoming
  • Chen, Zhangxin

Abstract

Geothermal energy is gaining global attractiveness owing to its abundance and sustainable nature. An in-depth understanding of potential geothermal production provides the energy industry a possibility to diversify the supply portfolio. With the development of artificial intelligence, machine learning offers an efficient alternative to the conventional numerical simulation method in forecasting energy harvesting. However, a comprehensive comparison and an effective algorithm selection are absent from the machine learning applications in forecasting geothermal energy recovery. In this study, four machine learning algorithms based data-driven models are created to determine the optimal choice in predicting geothermal production, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN). To investigate their application range, two different sizes of data groups are involved to train and test these models, and their performance is comprehensively compared. As the results show, the highest coefficient of determination R2 of 0.998 is demonstrated in the ANN models showing its promising predictive ability. Besides, the ANN is the most stable with the lowest performance variances between a training set and a validation set. In addition, the ANN is the most adaptable due to its minimal performance differences between different sizes of data groups. By jointly considering the prediction accuracy, stability and adaptability, the ANN is the best choice to substitute numerical simulation for predicting geothermal development. Importantly, the successful implementation of the proposed data-driven model requires 2700 times less computational time compared to numerical simulation, demonstrating a considerable improvement in the prediction efficiency. The results provide a beneficial reference for operators in conducting machine learning to simulate the development of the geothermal system studied, and can be effectively applied in other energy systems.

Suggested Citation

  • Xue, Zhenqian & Zhang, Kai & Zhang, Chi & Ma, Haoming & Chen, Zhangxin, 2023. "Comparative data-driven enhanced geothermal systems forecasting models: A case study of Qiabuqia field in China," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016493
    DOI: 10.1016/j.energy.2023.128255
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223016493
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128255?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Liang & Ezekiel, Justin & Li, Dexiang & Pei, Jingjing & Ren, Shaoran, 2014. "Potential assessment of CO2 injection for heat mining and geological storage in geothermal reservoirs of China," Applied Energy, Elsevier, vol. 122(C), pages 237-246.
    2. Gong, Facheng & Guo, Tiankui & Sun, Wei & Li, Zhaomin & Yang, Bin & Chen, Yimei & Qu, Zhanqing, 2020. "Evaluation of geothermal energy extraction in Enhanced Geothermal System (EGS) with multiple fracturing horizontal wells (MFHW)," Renewable Energy, Elsevier, vol. 151(C), pages 1339-1351.
    3. Liu, Gang & Zhou, Chunwei & Rao, Zhenghua & Liao, Shengming, 2021. "Impacts of fracture network geometries on numerical simulation and performance prediction of enhanced geothermal systems," Renewable Energy, Elsevier, vol. 171(C), pages 492-504.
    4. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    5. Zhang, Yu & Zhang, Yanjun & Zhou, Ling & Lei, Zhihong & Guo, Liangliang & Zhou, Jian, 2022. "Reservoir stimulation design and evaluation of heat exploitation of a two-horizontal-well enhanced geothermal system (EGS) in the Zhacang geothermal field, Northwest China," Renewable Energy, Elsevier, vol. 183(C), pages 330-350.
    6. Chen, Jiliang & Jiang, Fangming, 2015. "Designing multi-well layout for enhanced geothermal system to better exploit hot dry rock geothermal energy," Renewable Energy, Elsevier, vol. 74(C), pages 37-48.
    7. Zhang, Wei & Qu, Zhanqing & Guo, Tiankui & Wang, Zhiyuan, 2019. "Study of the enhanced geothermal system (EGS) heat mining from variably fractured hot dry rock under thermal stress," Renewable Energy, Elsevier, vol. 143(C), pages 855-871.
    8. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    9. Ko, Chia-Nan & Lee, Cheng-Ming, 2013. "Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter," Energy, Elsevier, vol. 49(C), pages 413-422.
    10. Xu, Tianfu & Yuan, Yilong & Jia, Xiaofeng & Lei, Yude & Li, Shengtao & Feng, Bo & Hou, Zhaoyun & Jiang, Zhenjiao, 2018. "Prospects of power generation from an enhanced geothermal system by water circulation through two horizontal wells: A case study in the Gonghe Basin, Qinghai Province, China," Energy, Elsevier, vol. 148(C), pages 196-207.
    11. Lei, Zhihong & Zhang, Yanjun & Yu, Ziwang & Hu, Zhongjun & Li, Liangzhen & Zhang, Senqi & Fu, Lei & Zhou, Ling & Xie, Yangyang, 2019. "Exploratory research into the enhanced geothermal system power generation project: The Qiabuqia geothermal field, Northwest China," Renewable Energy, Elsevier, vol. 139(C), pages 52-70.
    12. Gerber, Léda & Maréchal, François, 2012. "Environomic optimal configurations of geothermal energy conversion systems: Application to the future construction of Enhanced Geothermal Systems in Switzerland," Energy, Elsevier, vol. 45(1), pages 908-923.
    13. Lu, Shyi-Min, 2018. "A global review of enhanced geothermal system (EGS)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2902-2921.
    14. Asai, Pranay & Panja, Palash & McLennan, John & Moore, Joseph, 2018. "Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters," Renewable Energy, Elsevier, vol. 122(C), pages 184-195.
    15. Zhang, Chao & Jiang, Guangzheng & Jia, Xiaofeng & Li, Shengtao & Zhang, Shengsheng & Hu, Di & Hu, Shengbiao & Wang, Yibo, 2019. "Parametric study of the production performance of an enhanced geothermal system: A case study at the Qiabuqia geothermal area, northeast Tibetan plateau," Renewable Energy, Elsevier, vol. 132(C), pages 959-978.
    16. Zeng, Yu-Chao & Wu, Neng-You & Su, Zheng & Wang, Xiao-Xing & Hu, Jian, 2013. "Numerical simulation of heat production potential from hot dry rock by water circulating through a novel single vertical fracture at Desert Peak geothermal field," Energy, Elsevier, vol. 63(C), pages 268-282.
    17. Biglari, Fahimeh, 2015. "Dynamic scaling on the limited memory BFGS method," European Journal of Operational Research, Elsevier, vol. 243(3), pages 697-702.
    18. Asai, Pranay & Panja, Palash & McLennan, John & Deo, Milind, 2019. "Effect of different flow schemes on heat recovery from Enhanced Geothermal Systems (EGS)," Energy, Elsevier, vol. 175(C), pages 667-676.
    19. Pei, Huafu & Song, Huaibo & Meng, Fanhua & Liu, Weiling, 2022. "Long-term thermomechanical displacement prediction of energy piles using machine learning techniques," Renewable Energy, Elsevier, vol. 195(C), pages 620-636.
    20. Shi, Yu & Song, Xianzhi & Song, Guofeng, 2021. "Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network," Applied Energy, Elsevier, vol. 282(PA).
    21. Lei, Zhihong & Zhang, Yanjun & Zhang, Senqi & Fu, Lei & Hu, Zhongjun & Yu, Ziwang & Li, Liangzhen & Zhou, Jian, 2020. "Electricity generation from a three-horizontal-well enhanced geothermal system in the Qiabuqia geothermal field, China: Slickwater fracturing treatments for different reservoir scenarios," Renewable Energy, Elsevier, vol. 145(C), pages 65-83.
    22. Asai, Pranay & Panja, Palash & McLennan, John & Moore, Joseph, 2019. "Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)," Renewable Energy, Elsevier, vol. 131(C), pages 763-777.
    23. Zhang, Kai & Lau, Hon Chung, 2022. "Sequestering CO2 as CO2 hydrate in an offshore saline aquifer by reservoir pressure management," Energy, Elsevier, vol. 239(PC).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xue, Zhenqian & Ma, Haoming & Wei, Yizheng & Wu, Wei & Sun, Zhe & Chai, Maojie & Zhang, Chi & Chen, Zhangxin, 2024. "Integrated technological and economic feasibility comparisons of enhanced geothermal systems associated with carbon storage," Applied Energy, Elsevier, vol. 359(C).
    2. Wang, Zhipeng & Ning, Zhengfu & Guo, Wenting & Zhan, Jie & Zhang, Yuanxin, 2024. "Study of fracture monitoring and heat extraction evaluation in geothermal reservoir modified by abandoned well pattern: Numerical models and case studies," Energy, Elsevier, vol. 296(C).
    3. Tong, Huidong & Chen, Youliang & Chen, Qijian & Du, Xi & Xiao, Peng & Wang, Suran & Dong, Yang & Pan, Yungui & Ma, Hao & Long, Zhiyu, 2023. "A true triaxial creep constitutive model of rock considering the coupled thermo-mechanical damage," Energy, Elsevier, vol. 285(C).
    4. Yu, Ruyang & Zhang, Kai & Ramasubramanian, Brindha & Jiang, Shu & Ramakrishna, Seeram & Tang, Yuhang, 2024. "Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China," Energy, Elsevier, vol. 296(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zheng, Jun & Li, Peng & Dou, Bin & Fan, Tao & Tian, Hong & Lai, Xiaotian, 2022. "Impact research of well layout schemes and fracture parameters on heat production performance of enhanced geothermal system considering water cooling effect," Energy, Elsevier, vol. 255(C).
    2. Ma, Yuanyuan & Li, Shibin & Zhang, Ligang & Liu, Songze & Liu, Zhaoyi & Li, Hao & Shi, Erxiu & Liu, Xuemei & Liu, Hongliang, 2020. "Analysis on the heat extraction performance of multi-well injection enhanced geothermal system based on leaf-like bifurcated fracture networks," Energy, Elsevier, vol. 213(C).
    3. Ma, Yuanyuan & Li, Shibin & Zhang, Ligang & Liu, Songze & Liu, Zhaoyi & Li, Hao & Shi, Erxiu, 2020. "Study on the effect of well layout schemes and fracture parameters on the heat extraction performance of enhanced geothermal system in fractured reservoir," Energy, Elsevier, vol. 202(C).
    4. Ma, Yuanyuan & Li, Shibin & Zhang, Ligang & Liu, Songze & Liu, Zhaoyi & Li, Hao & Shi, Erxiu & Zhang, Haijun, 2020. "Numerical simulation study on the heat extraction performance of multi-well injection enhanced geothermal system," Renewable Energy, Elsevier, vol. 151(C), pages 782-795.
    5. Li, Xinxin & Li, Chengyu & Gong, Wenping & Zhang, Yanjie & Wang, Junchao, 2023. "Probabilistic analysis of heat extraction performance in enhanced geothermal system based on a DFN-based modeling scheme," Energy, Elsevier, vol. 263(PC).
    6. Cao, Wenjiong & Huang, Wenbo & Chen, Juanwen & Li, Zhibin & Jiang, Fangming, 2023. "Numerical study on the heat extraction performance of enhanced geothermal systems with a well-fracture-reservoir combined model," Renewable Energy, Elsevier, vol. 202(C), pages 370-380.
    7. Qiao, Mingzheng & Jing, Zefeng & Feng, Chenchen & Li, Minghui & Chen, Cheng & Zou, Xupeng & Zhou, Yujuan, 2024. "Review on heat extraction systems of hot dry rock: Classifications, benefits, limitations, research status and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    8. Xue, Zhenqian & Ma, Haoming & Wei, Yizheng & Wu, Wei & Sun, Zhe & Chai, Maojie & Zhang, Chi & Chen, Zhangxin, 2024. "Integrated technological and economic feasibility comparisons of enhanced geothermal systems associated with carbon storage," Applied Energy, Elsevier, vol. 359(C).
    9. Zhang, Yu & Zhang, Yanjun & Zhou, Ling & Lei, Zhihong & Guo, Liangliang & Zhou, Jian, 2022. "Reservoir stimulation design and evaluation of heat exploitation of a two-horizontal-well enhanced geothermal system (EGS) in the Zhacang geothermal field, Northwest China," Renewable Energy, Elsevier, vol. 183(C), pages 330-350.
    10. Yu, Ruyang & Zhang, Kai & Ramasubramanian, Brindha & Jiang, Shu & Ramakrishna, Seeram & Tang, Yuhang, 2024. "Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China," Energy, Elsevier, vol. 296(C).
    11. Meng, Nan & Li, Tailu & Wang, Jianqiang & Jia, Yanan & Liu, Qinghua & Qin, Haosen, 2020. "Synergetic mechanism of fracture properties and system configuration on techno-economic performance of enhanced geothermal system for power generation during life cycle," Renewable Energy, Elsevier, vol. 152(C), pages 910-924.
    12. Xiang Gao & Tailu Li & Yao Zhang & Xiangfei Kong & Nan Meng, 2022. "A Review of Simulation Models of Heat Extraction for a Geothermal Reservoir in an Enhanced Geothermal System," Energies, MDPI, vol. 15(19), pages 1-23, September.
    13. Zhang, Qitao & Dahi Taleghani, Arash, 2023. "Autonomous fracture flow tunning to enhance efficiency of fractured geothermal systems," Energy, Elsevier, vol. 281(C).
    14. Yang, Fujian & Wang, Guiling & Hu, Dawei & Liu, Yanguang & Zhou, Hui & Tan, Xianfeng, 2021. "Calibrations of thermo-hydro-mechanical coupling parameters for heating and water-cooling treated granite," Renewable Energy, Elsevier, vol. 168(C), pages 544-558.
    15. Hou, Xinglan & Zhong, Xiuping & Nie, Shuaishuai & Wang, Yafei & Tu, Guigang & Ma, Yingrui & Liu, Kunyan & Chen, Chen, 2024. "Study on the heat recovery behavior of horizontal well systems in the Qiabuqia geothermal area of the Gonghe Basin, China," Energy, Elsevier, vol. 286(C).
    16. Zhou, Luming & Zhu, Zhende & Xie, Xinghua & Hu, Yunjin, 2022. "Coupled thermal–hydraulic–mechanical model for an enhanced geothermal system and numerical analysis of its heat mining performance," Renewable Energy, Elsevier, vol. 181(C), pages 1440-1458.
    17. Wu, Xiaotian & Yu, Likui & Hassan, N.M.S. & Ma, Weiwu & Liu, Gang, 2021. "Evaluation and optimization of heat extraction in enhanced geothermal system via failure area percentage," Renewable Energy, Elsevier, vol. 169(C), pages 204-220.
    18. Wang, Jiacheng & Zhao, Zhihong & Liu, Guihong & Xu, Haoran, 2022. "A robust optimization approach of well placement for doublet in heterogeneous geothermal reservoirs using random forest technique and genetic algorithm," Energy, Elsevier, vol. 254(PC).
    19. Yuan Zhao & Lingfeng Shu & Shunyi Chen & Jun Zhao & Liangliang Guo, 2022. "Optimization Design of Multi-Factor Combination for Power Generation from an Enhanced Geothermal System by Sensitivity Analysis and Orthogonal Test at Qiabuqia Geothermal Area," Sustainability, MDPI, vol. 14(12), pages 1-35, June.
    20. Hu, Xincheng & Banks, Jonathan & Guo, Yunting & Liu, Wei Victor, 2022. "Utilizing geothermal energy from enhanced geothermal systems as a heat source for oil sands separation: A numerical evaluation," Energy, Elsevier, vol. 238(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016493. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.