IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v361y2024ics0306261924002976.html
   My bibliography  Save this article

Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning

Author

Listed:
  • Mao, Shaowen
  • Chen, Bailian
  • Malki, Mohamed
  • Chen, Fangxuan
  • Morales, Misael
  • Ma, Zhiwei
  • Mehana, Mohamed

Abstract

Underground hydrogen (H2) storage (UHS) has emerged as a promising technology to facilitate the widespread adoption of fluctuating renewable energy sources. However, the current UHS experience primarily focuses on salt caverns, with no working examples of storing pure H2 in porous reservoirs. A key challenge in UHS within porous rocks is the uncertainty in evaluating storage performance due to complicated geological and operational conditions. While physics-based reservoir simulations are commonly used to quantify H2 injection and withdrawal processes during storage cycles, they are computationally demanding and unsuitable for providing rapid support to UHS operations. In this study, we develop efficient reduced-order models (ROMs) for UHS in depleted natural gas reservoirs using deep neural networks (DNNs) based on comprehensive reservoir simulation data sets. The ROMs can accurately forecast UHS performance metrics (H2 withdrawal efficiency, produced H2 purity, produced gas-water ratio) across various geological and operational conditions and are over 22000 times faster than physics-based simulations. Then, we employ the ROMs for sensitivity analysis to assess the impact of geological and operational parameters on UHS performance and conduct uncertainty quantification to characterize potential performance and associated probabilities. Lastly, we present a field case study from the Dakota formation of the Basin field in the Intermountain-West (I-WEST) region, USA. Based on the ROMs’ predictions, Dakota formation is favorable for UHS due to its high H2 withdrawal efficiency and purity, and low water production risk. By optimizing operational parameters, we can further improve the storage performance in Dakota formation and reduce the uncertainty in UHS performance prediction. This study introduces an efficient ROM-based approach to assess and optimize UHS performance, supporting the development of effective UHS projects in depleted gas reservoirs.

Suggested Citation

  • Mao, Shaowen & Chen, Bailian & Malki, Mohamed & Chen, Fangxuan & Morales, Misael & Ma, Zhiwei & Mehana, Mohamed, 2024. "Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002976
    DOI: 10.1016/j.apenergy.2024.122914
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924002976
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.122914?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.

    Citations

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


    Cited by:

    1. Wenjun Zhao & Tangyan Liu & Jian Yang & Zhuo Zhang & Cheng Feng & Jizhou Tang, 2024. "Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation," Sustainability, MDPI, vol. 16(7), pages 1-23, March.

    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:appene:v:361:y:2024:i:c:s0306261924002976. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.