IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v139y2019icp873-894.html
   My bibliography  Save this article

A robust numerical method for modeling multiple wells in city-scale geothermal field based on simplified one-dimensional well model

Author

Listed:
  • Wang, Guiling
  • Liu, Guihong
  • Zhao, Zhihong
  • Liu, Yanguang
  • Pu, Hai

Abstract

An efficient numerical model, which can simulate the coupled fluid flow and heat transfer processes in geothermal fields, is essentially required to evaluate the fate of geothermal wells and the temperature and pressure evolutions of geothermal reservoirs in response to the long-term well operations. Due to the scale disparity between wells and geothermal reservoirs (∼dm vs. ∼km), very fine mesh is required to accurately represent the highly-dynamic zones near geothermal wells, and thus the conventional geothermal reservoir model including geothermal wells is usually time consuming. To improve the computational efficiency without losing accuracy, a simplified one-dimensional geothermal well model considering heat convection and conduction along well axis and heat transfer between geothermal fluid and rocks in the radial direction is proposed and incorporated into the geothermal reservoir model. Both a bench-mark example and a case study of Beijing city geothermal field are presented to demonstrate the reasonability and efficiency of the proposed reservoir modeling method. The computational time are significantly reduced because of avoiding mesh refinement near geothermal wells. The multiple wells effects, interactions between different reservoirs, and the role of faults in reservoir performance are also discussed based on the case study of Beijing city geothermal field.

Suggested Citation

  • Wang, Guiling & Liu, Guihong & Zhao, Zhihong & Liu, Yanguang & Pu, Hai, 2019. "A robust numerical method for modeling multiple wells in city-scale geothermal field based on simplified one-dimensional well model," Renewable Energy, Elsevier, vol. 139(C), pages 873-894.
  • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:873-894
    DOI: 10.1016/j.renene.2019.02.131
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119302964
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2019.02.131?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. Bujakowski, Wiesław & Tomaszewska, Barbara & Miecznik, Maciej, 2016. "The Podhale geothermal reservoir simulation for long-term sustainable production," Renewable Energy, Elsevier, vol. 99(C), pages 420-430.
    2. 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.
    3. Carotenuto, Alberto & Ciccolella, Michela & Massarotti, Nicola & Mauro, Alessandro, 2016. "Models for thermo-fluid dynamic phenomena in low enthalpy geothermal energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 330-355.
    4. Feng, Guanhong & Xu, Tianfu & Gherardi, Fabrizio & Jiang, Zhenjiao & Bellani, Stefano, 2017. "Geothermal assessment of the Pisa plain, Italy: Coupled thermal and hydraulic modeling," Renewable Energy, Elsevier, vol. 111(C), pages 416-427.
    5. International Finance Corporation, 2013. "Success of Geothermal Wells : A Global Study," World Bank Publications - Reports 16493, The World Bank Group.
    6. 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.
    7. Saeid, Sanaz & Al-Khoury, Rafid & Nick, Hamidreza M. & Hicks, Michael A., 2015. "A prototype design model for deep low-enthalpy hydrothermal systems," Renewable Energy, Elsevier, vol. 77(C), pages 408-422.
    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. Liu, Guihong & Zhao, Zhihong & Xu, Haoran & Zhang, Jinping & Kong, Xiangjun & Yuan, Lijuan, 2022. "A robust assessment method of recoverable geothermal energy considering optimal development parameters," Renewable Energy, Elsevier, vol. 201(P1), pages 426-440.
    2. Liu, Guihong & Wang, Guiling & Zhao, Zhihong & Ma, Feng, 2020. "A new well pattern of cluster-layout for deep geothermal reservoirs: Case study from the Dezhou geothermal field, China," Renewable Energy, Elsevier, vol. 155(C), pages 484-499.
    3. Li, Shengtao & Wen, Dongguang & Feng, Bo & Li, Fengyu & Yue, Dongdong & Zhang, Qiuxia & Wang, Junzhao & Feng, Zhaolong, 2023. "Numerical optimization of geothermal energy extraction from deep karst reservoir in North China," Renewable Energy, Elsevier, vol. 202(C), pages 1071-1085.

    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. Ma, Weiwu & Wang, Yadan & Wu, Xiaotian & Liu, Gang, 2020. "Hot dry rock (HDR) hydraulic fracturing propagation and impact factors assessment via sensitivity indicator," Renewable Energy, Elsevier, vol. 146(C), pages 2716-2723.
    2. Liu, Guihong & Wang, Guiling & Zhao, Zhihong & Ma, Feng, 2020. "A new well pattern of cluster-layout for deep geothermal reservoirs: Case study from the Dezhou geothermal field, China," Renewable Energy, Elsevier, vol. 155(C), pages 484-499.
    3. 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).
    4. Wang, Jiacheng & Tan, Xianfeng & Zhao, Zhihong & Chen, Jinfan & He, Jie & Shi, Qipeng, 2024. "Coupled thermo-hydro-mechanical modeling on geothermal doublet subject to seasonal exploitation and storage," Energy, Elsevier, vol. 293(C).
    5. 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).
    6. 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).
    7. Salimzadeh, S. & Grandahl, M. & Medetbekova, M. & Nick, H.M., 2019. "A novel radial jet drilling stimulation technique for enhancing heat recovery from fractured geothermal reservoirs," Renewable Energy, Elsevier, vol. 139(C), pages 395-409.
    8. Guo, Tiankui & Tang, Songjun & Sun, Jiang & Gong, Facheng & Liu, Xiaoqiang & Qu, Zhanqing & Zhang, Wei, 2020. "A coupled thermal-hydraulic-mechanical modeling and evaluation of geothermal extraction in the enhanced geothermal system based on analytic hierarchy process and fuzzy comprehensive evaluation," Applied Energy, Elsevier, vol. 258(C).
    9. Aliyu, Musa D. & Archer, Rosalind A., 2021. "Numerical simulation of multifracture HDR geothermal reservoirs," Renewable Energy, Elsevier, vol. 164(C), pages 541-555.
    10. Gkousis, Spiros & Welkenhuysen, Kris & Harcouët-Menou, Virginie & Pogacnik, Justin & Laenen, Ben & Compernolle, Tine, 2024. "Integrated geo-techno-economic and real options analysis of the decision to invest in a medium enthalpy deep geothermal heating plant. A case study in Northern Belgium," Energy Economics, Elsevier, vol. 134(C).
    11. Asai, Pranay & Podgorney, Robert & McLennan, John & Deo, Milind & Moore, Joseph, 2022. "Analytical model for fluid flow distribution in an Enhanced Geothermal Systems (EGS)," Renewable Energy, Elsevier, vol. 193(C), pages 821-831.
    12. 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).
    13. 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).
    14. Martina Gizzi & Federico Vagnon & Glenda Taddia & Stefano Lo Russo, 2023. "A Review of Groundwater Heat Pump Systems in the Italian Framework: Technological Potential and Environmental Limits," Energies, MDPI, vol. 16(12), pages 1-13, June.
    15. Sowizdzal, Anna, 2018. "Geothermal energy resources in Poland – Overview of the current state of knowledge," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4020-4027.
    16. Song, Xianzhi & Shi, Yu & Li, Gensheng & Shen, Zhonghou & Hu, Xiaodong & Lyu, Zehao & Zheng, Rui & Wang, Gaosheng, 2018. "Numerical analysis of the heat production performance of a closed loop geothermal system," Renewable Energy, Elsevier, vol. 120(C), pages 365-378.
    17. Santamarta, Juan C. & García-Gil, Alejandro & Expósito, María del Cristo & Casañas, Elías & Cruz-Pérez, Noelia & Rodríguez-Martín, Jesica & Mejías-Moreno, Miguel & Götzl, Gregor & Gemeni, Vasiliki, 2021. "The clean energy transition of heating and cooling in touristic infrastructures using shallow geothermal energy in the Canary Islands," Renewable Energy, Elsevier, vol. 171(C), pages 505-515.
    18. Yin Yuan & Weiqing Li & Jiawen Zhang & Junkai Lei & Xianghong Xu & Lihan Bian, 2024. "A Novel Geothermal Wellbore Model Based on the Drift-Flux Approach," Energies, MDPI, vol. 17(14), pages 1-17, July.
    19. Guilin Zhu & Linyou Zhang & Zhihui Deng & Qingda Feng & Zhaoxuan Niu & Wenhao Xu, 2023. "Three-Dimensional Geological Modeling and Resource Estimation of Hot Dry Rock in the Gonghe Basin, Qinghai Province," Energies, MDPI, vol. 16(16), pages 1-16, August.
    20. Hashemian, Nasim & Noorpoor, Alireza, 2022. "A geothermal-biomass powered multi-generation plant with freshwater and hydrogen generation options: Thermo-economic-environmental appraisals and multi-criteria optimization," Renewable Energy, Elsevier, vol. 198(C), pages 254-266.

    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:renene:v:139:y:2019:i:c:p:873-894. 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/renewable-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.