Release Note v0.1.0#
Author: Zakariya Abugrin | Date: May 2025
Hi 🙋‍♂️,
I am Zakariya Abugrin, the author of ReservoirFlow.
During the last few years, I have been experiencing the immense joy of developing reservoir simulation models in Python where I can seamlessly investigate the beautiful combination of scientific computing with deep learning (e.g. physics informed neural networks). Combining reservoir engineering and data science in the Python ecosystem is phenomenal and fun. I am indeed extremely lucky to be able to do that. Today, I am happy to finally share my passion in the form of a new library called ReservoirFlow. This is the first version where ReservoirFlow was released to the world, a Reservoir Simulation and Engineering Library in Python.
After working for several years on this project and sharing my progress on LinkedIn, publishing this library is indeed a unique moment for me. This point was already delayed several times due to the huge technical and scientific challenges associated with reservoir simulation especially when combined with deep learning. Nevertheless, this is still a uniquely interesting time since scientific machine learning is still a new emerging field that requires more practical applications which might be offered by this tool. I am very excited about the upcoming opportunities that will be opened by this library. I am looking forward to combining the world of scientific computing with deep learning to further develop more practical solutions.
There are many goals for this library, but one of the main goals is to provide open-source, accessible, and reproducible research that can benefit engineers and students at all levels from bachelor to PhD levels from different backgrounds, especially from Applied Mathematics and Physics (i.e. Mathematical Physics) and Petroleum Engineering (i.e. Reservoir Engineering). In addition, a special attention will be given to extending this library to offer solutions for projects related to reducing green house gas emissions such as \(CO_2\) underground storage.
The research on the topic of reservoir simulation is extremely rich but has been missing a tool where important papers are implemented in a standard, accessible and clean way that can be understood by others. We hope that we can provide such material in this platform to motivate students and researchers to use this tool in their research and also professors and academics in their lectures. Furthermore, we highly appreciate your support, especially by giving a proper credit to this work using the following citation:
ReservoirFlow: Reservoir Simulation and Engineering Library in Python, Zakariya Abugrin at Hiesab (2025)
Reservoir simulation books are very challenging to understand since they combine a wide range of topics from applied mathematics to programming and engineering. However, most of these books lack hands-on coding examples. Perhaps this is the reason why there are not many open-source tools for reservoir simulation. As a result, there is a huge gap between reservoir simulation books and how things actually work when it comes to implementing these concepts.
Reservoir simulation itself is indeed an ART that sometimes seems to be impossible to master. Therefore, alongside this library, I decided to write a book to explain the topic of reservoir simulation with many details supported by code examples from ReservoirFlow. However, this project has to be postponed until an acceptable version of ReservoirFlow is reached.
My passion now is to keep developing this tool but with the support of the community to accelerate its development. I also hope that this tool will make it easy for students and engineers to understand the phenomenon of fluid flow in porous media. Perhaps, one day we can form a big community around ReservoirFlow where state-of-the-art research focusing on combining scientific computing with scientific machine learning is openly shared and discussed; a mission that Hiesab as company will fully support and foster by providing resources, hosting collaborative events, and developing open-source tools.
Tip
If you are still wondering Why ReservoirFlow will be need, feel free to read Why do we need ReservoirFlow? 🤷‍♂️.
Dedication#
This project could have failed in many ways, but the support I got from all my family and friends was much bigger than that. This work took more than 5 years (from 2020 to 2025) of dedication, hard work, designing, engineering, and sometimes frustration aiming for perfection. I am glad that this project came to life in a best possible way; an open-source software.
Following the advice of my mother (Saadat Abugazia) who repeatedly asked me to build something useful for humanity, and the path that was drawn by my father (Yousef Abugrin), may God have mercy on him, who continuously insisted on taking private classes in English, Mathematics and Physics (in my free-time) from preparatory school to university time, I believe that my parents are the true heroes behind this work. I would like also to thank my wife and kids who have been extremely patient with me spending several years fighting with my black-screen coding. I would like also to thank my father-in-law Mohamed Bayoud for his continuous support.
I would also like to thank all my supervisors, teachers, and colleagues during all my studies and work specially my B.Sc supervisor Dr. Mohsen Khazam and examiners Dr. Nagmeddin Arifi and Dr. Abdulhamid Warieth (may God have mercy on him). I was lucky to have many great colleagues in studies and in the oil and gas industry such as Emad Kaawan, Rami Elhajjaji, and Ayoub Sherik. I would like also to thank my M.Sc supervisor Leonhard Ganzer for his great insights around reservoir engineering and simulation.
I would like to thank 3 women who helped me to achieve my dreams during my second master’s degree in Applied Data Science at Frankfurt School: Jo Karajanov, Zorica Zujic, and Ruby Salvatore. I can’t thank enough those great people. In addition, my supervisors and teachers at Frankfurt School including: Florian Ellsaesser, Levente Szabados, András Simonyi, Jan Nagler, Vahe Andonians, Gregory Wheeler, Kerem Tomak, and Peter Rossbach.
Last but not least, I would like to thank my supervisors from Friedrich-Alexander-Universität Erlangen-Nürnberg for my on going PhD in applied mathematics (focused on mathematical physics) including: Prof. Nadja Ray for her continues research support and guidance and Prof. Eberhard Bänsch for his support to start my PhD.
Contributors#
Zakariya Abugrin (Author).
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