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Article summary:
1. This article introduces a physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media.
2. The model takes advantage of the spatial topology predictive capability of convolutional neural networks and decomposes 3D reservoir domains into manageable small 2D layer-wise images.
3. The approach has been applied to predict the temporal-spatial evolution of state variables such as pressure and saturation, and has been used to history match and predict two-phase flow in 2D-transects of channelized reservoirs.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed overview of the physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media. It presents both the advantages and limitations of this approach, as well as its potential applications in predicting temporal-spatial evolution of state variables such as pressure and saturation. Furthermore, it cites relevant research papers to support its claims, which adds to its credibility.
However, there are some points that could be improved upon. For example, the article does not provide any evidence or data to back up its claims about the effectiveness of this approach in predicting two-phase flow in 2D transects of channelized reservoirs. Additionally, it does not explore any counterarguments or alternative approaches that could be used instead of this one. Finally, it does not mention any possible risks associated with using this approach or how they can be mitigated.