Chair of Drilling and Completion Engineering (590)

Organisational unit: Chair

Research output

  1. 2025
  2. Published

    Detecting downhole drilling events EP 4 189 206 B1

    Elmgerbi, A. (Inventor) & Thonhauser, G. (Inventor), 2025, Patent No. EP 4 189 206 B1

    Research output: Patent

  3. 2024
  4. Published
  5. Published

    Sand screens for controlling sand production from hydrocarbon wells: A mini-review

    Kumar, S., Kumar, G., Krishna, S., Kumar, A., Gupta, T., Goel, P. N. & Kumari, S., Sept 2024, In: Geoenergy science and engineering. 240.2024, September, 19 p., 213040.

    Research output: Contribution to journalReview articlepeer-review

  6. Published
  7. Published

    Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models

    Krishna, S., Irfan, S. A., Keshavarz, S., Thonhauser, G. & Umer Ilyas, S., 24 Jul 2024, In: Multiscale and multidisciplinary modeling, experiments and design. 7.2024, 6, p. 5611-5630 20 p.

    Research output: Contribution to journalArticleResearchpeer-review

  8. E-pub ahead of print

    Evaluating Multi-target Regression Framework for Dynamic Condition Prediction in Wellbore

    Keshavarz, S., Elmgerbi, A., Vita, P. & Thonhauser, G., 23 Apr 2024, (E-pub ahead of print) In: The Arabian journal for science and engineering. 49.2024, June, p. 8953-8982 30 p.

    Research output: Contribution to journalArticleResearchpeer-review

  9. Published

    Deep reinforcement learning algorithm for wellbore cleaning across drilling operation

    Keshavarz, S., Elmgerbi, A. & Thonhauser, G., 25 Mar 2024, Fourth EAGE Digitalization Conference & Exhibition, Mar 2024, Volume 2024, p.1 - 5. Vol. 2024.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  10. Published
  11. Published
  12. Published
  13. Published
  14. Published

    Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

    Jason, C., Umer Ilyas, S., Ridha, S., Sehar, U., Alsaady, M. & Krishna, S., 2024, Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. p. 469-477 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  15. Published
  16. Published

    Wellbore Health Characterization for Drilling Optimization

    Les, B., 2024

    Research output: ThesisMaster's Thesis

  17. 2023
  18. Published
  19. Published

    An Interdisciplinary Approach to Investigate the Cement Integrity for Underground Hydrogen Storage Wells

    Nasiri, A., Ravi, K., Prohaska-Marchried, M., Feichter, M., Raith, J. & Pruno, S., 5 Jun 2023, SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition. 29 p. SPE-214423-MS

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  20. Published

    Cellulose nanocrystals (CNCs) as a potential additive for improving API class G cement performance: An experimental study

    Elmgerbi, A., Abou Askar, I., Fine, A., Thonhauser, G. & Ashena, R., Jun 2023, In: Natural Gas Industry. B. 10.2023, 3, p. 233-244 12 p.

    Research output: Contribution to journalArticleResearchpeer-review

  21. Published

    Single energy Micro-computed tomography(µCT): A reliable potential alternative to mineral Investigation of formation rock

    Nasiri, A., 26 May 2023.

    Research output: Contribution to conferencePosterResearchpeer-review

  22. Published

    Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid

    Cai Ning, Y., Ridha, S., Umer Ilyas, S., Krishna, S., Dzulkarnain, I. & Abdurraham, M., Apr 2023, In: Journal of Petroleum Exploration and Production Technology. 13.2023, April, p. 1031-1052 22 p.

    Research output: Contribution to journalArticleResearchpeer-review

  23. Published

    A Reinforcement Learning Approach for Real-Time Autonomous Decision-Making in Well Construction

    Keshavarz, S., Vita, P., Rückert, E., Ortner, R. & Thonhauser, G., 19 Jan 2023, SPE AI Symposium 2023: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry. (Society of Petroleum Engineers - SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, AIS 2023).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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