Abstract
In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. Practical examples about water-driven hypotonic reservoirs are provided to demonstrate the correctness of the method and its ability to meet the requirements for practical reservoir applications.
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