IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v250y2025ics096014812500922x.html

Real-time incident detection in geothermal drilling through machine learning

Author

Listed:
  • Aspiras, Aira H.
  • Zarrouk, Sadiq J.
  • Winmill, Ralph
  • Kempa-Liehr, Andreas W.

Abstract

Geothermal energy, while a reliable baseload low-carbon resource, only comprise a small fraction of global renewable capacity due to high upfront costs and resource risks. Drilling wells accounts for ∼60 % of capital investment costs, thus finishing wells on-time and within budget has always been a crucial challenge for operators and challenges like fault structures, severe lost circulation, and high temperatures inherent to geothermal systems make this difficult. Early detection is crucial in taking corrective actions before problems escalate and leveraging machine learning (ML) technologies offers the potential to identify patterns that precede hole-related non-productive time incidents, such as stuckpipes or borehole instability.

Suggested Citation

  • Aspiras, Aira H. & Zarrouk, Sadiq J. & Winmill, Ralph & Kempa-Liehr, Andreas W., 2025. "Real-time incident detection in geothermal drilling through machine learning," Renewable Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:renene:v:250:y:2025:i:c:s096014812500922x
    DOI: 10.1016/j.renene.2025.123260
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812500922X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.123260?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Javed Akbar Khan & Muhammad Irfan & Sonny Irawan & Fong Kam Yao & Md Shokor Abdul Rahaman & Ahmad Radzi Shahari & Adam Glowacz & Nazia Zeb, 2020. "Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents," Energies, MDPI, vol. 13(14), pages 1-26, July.
    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. Shan, Kun & Cong, Lianghan & Yu, Ziwang & Ye, Xiaoqi, 2026. "Artificial intelligence empowering geothermal energy development: A full-lifecycle review from exploration to operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).

    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. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.
    2. Aditi Nautiyal & Amit Kumar Mishra, 2025. "Machine learning approach for intelligent prediction of petroleum upstream stuck pipe challenge in oil and gas industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(10), pages 24167-24193, October.
    3. Mohammad Kamal Asif Khan & Javed Akbar Khan & Habib Ullah & Hussain H. Al-Kayiem & Sonny Irawan & Muhammad Irfan & Adam Glowacz & Hui Liu & Witold Glowacz & Saifur Rahman, 2021. "De-Emulsification and Gravity Separation of Micro-Emulsion Produced with Enhanced Oil Recovery Chemicals Flooding," Energies, MDPI, vol. 14(8), pages 1-14, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:250:y:2025:i:c:s096014812500922x. 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.