Gerhard Thonhauser

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Research output

  1. 2024
  2. 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 journal › Article › Research › peer-review

  3. 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 journal › Article › Research › peer-review

  4. 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 proceeding › Conference contribution

  5. 2023
  6. 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 journal › Article › Research › peer-review

  7. 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 proceeding › Conference contribution

  8. 2022
  9. E-pub ahead of print

    Ultrasound velocity profiling technique for in-line rheological measurements: A prospective review

    Krishna, S., Thonhauser, G., Kumar, S., Elmgerbi, A. & Ravi, K., 2 Nov 2022, (E-pub ahead of print) In: Measurement. 205.2022, December, 19 p., 112152.

    Research output: Contribution to journal › Article › Research › peer-review

  10. E-pub ahead of print

    Holistic autonomous model for early detection of downhole drilling problems in real-time

    Elmgerbi, A. & Thonhauser, G., 20 Jun 2022, (E-pub ahead of print) In: Process safety and environmental protection. 164.2022, August, p. 418-434 17 p.

    Research output: Contribution to journal › Article › Research › peer-review

  11. Published

    Machine Learning Techniques Application for Real-Time Drilling Hydraulic Optimization

    Elmgerbi, A., Thonhauser, G., Nascimento, A. & Chuykov, E., 21 Feb 2022.

    Research output: Contribution to conference › Paper

  12. Published

    DETECTING DOWNHOLE DRILLING EVENTS

    Elmgerbi, A. & Thonhauser, G., 3 Feb 2022, IPC No. E21B 47/ 06 A I, Patent No. WO2022022812, Priority date 28 Jul 2020, Priority No. WO2020EP71284

    Research output: Patent

  13. 2021
  14. Published

    Implementing the autonomous adaptive algorithm to manage ESP operation in harsh reservoir conditions

    Antonic, M., Solesa, M., Thonhauser, G., Aleksic, M. & Zolotukhin, A., 25 Nov 2021.

    Research output: Contribution to conference › Paper › peer-review

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