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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Ben Aoun, M. A., Pasquier, P., & Nguyen, A. (2026). Numerical and LSTM-based modeling of physical clogging effects in geothermal injection wells. Geothermics, 138, 103661 (15 pages). External link
Ben Aoun, M. A., Pasquier, P., Nguyen, A., & Rose, C. (2024, September). Applying CNN-LSTM Neural Network To Predict Water Levels in Injection Wells [Poster]. World Groundwater Congress (IAH 2024), Davos, Switzerland (1 page). Unavailable
Ben Aoun, M. A., Pasquier, P., & Nguyen, A. (2024). On the use of an underground storage tank for disposing bleed water in standing column well systems. Geothermics, 119, 102969 (11 pages). External link
Ben Aoun, M. A., Nguyen, A., & Pasquier, P. (2024, May). Reduction of injection flow rate with an underground buffer tank coupled with a standing column well [Paper]. IGSHPA Research Conference 2024, Montréal, Québec (10 pages). External link
Ben Aoun, M. A., & Madarász, T. (2022). Applying machine learning to predict the rate of penetration for geothermal drilling located in the Utah FORGE site. Energies, 15(12), 21 pages. External link
