ReservoirFlow is a modern open-source Python library developed by Zakariya Abugrin at Hiesab. ReservoirFlow is designed to study and model the process of fluid flow in porous media related to subsurface energy storage systems, reservoir simulation and engineering. ReservoirFlow is the first reservoir simulator based on physics-informed neural network models and one of its kind in a sense that it allows comparing and combining analytical solutions, numerical solutions, and neurical solutions (i.e. solutions based on artificial neural networks). ReservoirFlow is planned to be a central platform between education and industry where scientific papers are implemented and distributed in a standard and accessible format with coding examples, tutorials, and trainings.
ReservoirFlow is designed based on the modern Python stack for data science, scientific computing, machine learning, and deep learning with the objective to support high-performance computing including multithreading, parallelism, GPU, and TPU. Throughout our computing problems based on large simulation models, intensive benchmarking well be carefully designed and carried out to evaluate the performance of computing software (i.e. frameworks) and hardware (e.g. GPUs). The outcome of these tests will be used to further improve the performance of ReservoirFlow and to provide materials with recommendations about available computing tools, techniques and frameworks. ReservoirFlow is planned to support different backends including NumPy, SciPy, JAX, PyTorch, TensorFlow, and more.
ReservoirFlow brings reservoir simulation and engineering to the Python ecosystem to
empower
automation in
intelligent fields where engineers and specialists can deploy their models in containers that
will
be ready to make
real-time optimization for any well in the field. In contrast to commercial black-box software
where
reservoir
simulation studies are relatively isolated, important actions can be immediately predicted and
made
available for
the field hardware to execute. A special attention well be given to provide solutions for
environmentally friendly
projects with a clear objective to reduce emissions. We are committed to extend our tools to
cover
the topic of
Carbon Capture and Storage (CCS) especially
ReservoirFlow aims to achieving a high quality open research for reservoir simulation and engineering to provide solutions that combine the strength of scientific computing with the power of deep learning for different applications such as: reverse computing, interpolation or extrapolation, etc. Below are few examples of the problems that will be tackled in the future:
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Real-time reservoir management and production optimization using Cloud Computing and IoT.
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Reinforcement learning to achieve better production strategies for specific goals (e.g. maximize recovery, accelerate production).
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History matching using machine learning.
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Advanced computing such as GPU, TPU and Quantum Computing.
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Scientific Machine learning using Physics-informed neural networks (PINNs) or DeepONets.
An open-source reservoir simulation and engineering library within the Python ecosystem is also very important to students, universities, researchers, engineers, and practitioners. Unlike the common monopolistic approach in the Oil and Gas industry where software is usually offered as a closed black-box at a high cost, we plan to make our tools accessible and freely available to everyone except for commercial-use where an explicit authorization will be required. We aim to offer our sponsors the commercial-use license with other benefits including trainings, custom features, studies, and more. On the other hand, our license allows universities, students, academics, and researchers to use our tools directly for teaching or publication just with a proper referencing. Therefore, the growth of this tool can only be taken as a positive growth for a new community that we try to create. However, this requires a huge support to meet the upcoming challenges that we are looking for, see Support Us.