Publikationen

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2024
Veröffentlicht
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, 213040.
Publikationen: Beitrag in Fachzeitschrift › Übersichtsartikel › (peer-reviewed)
Veröffentlicht
Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models
Krishna, S., Irfan, S. A., Keshavarz, S., Thonhauser, G. & Ilyas, S. U., 24 Juli 2024, in: Multiscale and multidisciplinary modeling, experiments and design.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
Elektronische Veröffentlichung vor Drucklegung.
Evaluating Multi-target Regression Framework for Dynamic Condition Prediction in Wellbore
Keshavarz, S., Elmgerbi, A., Vita, P. & Thonhauser, G., 23 Apr. 2024, (Elektronische Veröffentlichung vor Drucklegung.) in: The Arabian journal for science and engineering. 49.2024, June, S. 8953-8982 30 S.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
Veröffentlicht
Deep reinforcement learning algorithm for wellbore cleaning across drilling operation
Keshavarz, S., Elmgerbi, A. & Thonhauser, G., 25 März 2024, Fourth EAGE Digitalization Conference & Exhibition, Mar 2024, Volume 2024, p.1 - 5. Band 2024.
Publikationen: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband
Veröffentlicht
Insight into carbon black and silicia fume as cement additives for geoenergy wells: Linking mineralogy to mechanical and physical properties
Sammer, T., Nasiri, A., Kostoglou, N., Ravi, K. & Raith, J., 2024, in: C – journal of carbon research. 2024, 10, S. 1-22
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
2023
Veröffentlicht
A fundamentals mineralogical investigation of downhole cements within the context of Underground Hydrogen Storage
Sammer, T., Ravi, K. & Raith, J., 11 Juli 2023.
Publikationen: Konferenzbeitrag › Poster › Forschung
Veröffentlicht
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., Juni 2023, in: Natural Gas Industry. B. 10.2023, 3, S. 233-244 12 S.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
Veröffentlicht
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, S. 1031-1052 22 S.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
Veröffentlicht
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).
Publikationen: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband
Veröffentlicht
Improved Predictive Model for Surge/Swab Pressure Estimation using a New Simplified (NS) Model
Ridha, S., Khanbesh, S., Arain, A. H., Yusuf, M. & Krishna, S., 2023, 2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023. Institute of Electrical and Electronics Engineers, (2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023).
Publikationen: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband

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Publikationen

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

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

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

Deep reinforcement learning algorithm for wellbore cleaning across drilling operation

Insight into carbon black and silicia fume as cement additives for geoenergy wells: Linking mineralogy to mechanical and physical properties

A fundamentals mineralogical investigation of downhole cements within the context of Underground Hydrogen Storage

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

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

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

Improved Predictive Model for Surge/Swab Pressure Estimation using a New Simplified (NS) Model

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