Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning

Water coning is one of the common issues in subsurface systems in which water flows into the production well through perforated zones. This phenomenon can cause severe problems in wellbore and surface facilities. Thus, accurate prediction of water breakthrough can help to adapt to the production mode and avoid such issues. Conducting flow simulations, as a conventional approach, can be very time demanding if one deals with large subsurface systems. Furthermore, several types of data are often collected during the life of a subsurface system each of which can help to predict the breakthrough and water coning. As such, it is very important to produce similar results using the time sequence data gathered from various geo-sensing tools. In this paper, a deep long short-term memory (LSTM) model is developed to predict the water cut and water breakthrough time for multiple production wells in a water flooding case. The dataset is generated by the Egg model with a multi-input-multi-output system. We found that the proposed model can capture the general trend of variation for the water cut time sequence data for a complex subsurface system. To evaluate the performance of our data-driven method, the results are compared with vanilla recurrent neural network (RNN), deep gated recurrent unit (GRU), and artificial neural network (ANN). The conducted comparison indicates that the proposed deep LSTM model outperforms the other three approaches when the results are compared with the numerical data.