Research output

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  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

    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

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