The Implementation of a Deep Neural Network (DNN) Approach in a Case Study Predicting the Distribution of Carbon Dioxide (CO2) Gas Saturation

Abstract

Predicting the distribution of CO2 gas saturation is one example of how multiphase flow might be evaluated in Carbon Capture and Storage (CCS). The TOUGH2 simulator is one of the numerical simulations commonly used for multiphase flow simulation. Ordinary numerical simulations have several issues, including high grid spatial resolution and high processing costs. One of the most effective deep learning approaches to predicting the distribution of CO2 gas saturation is the deep neural network (DNN). A deep neural network is a network with three interconnected layers, there are input, hidden, and output layers. DNN learns about the previously constructed architecture from the input data. DNN requires a large quantity of data as input. Thus, in this study, we use 700 data points for each of the train_a and train_b variables. The distribution of CO2 gas saturation will be predicted automatically by the trained DNN model. This technique can handle complex data patterns, such as gas saturation in multiphase flow problems. The reconstruction loss findings show that the loss value decreases as the number of epochs increases. Furthermore, we used 3 and 4 epochs to determine the difference in results between the two. As a result, the model with 4 epochs and 10−3 regularization weight obtained the lowest error value of 0.4305. In summary, this model is capable of predicting CO2 gas saturation distribution, but more research is needed to produce more optimal results. This research hopes to help monitor multiphase flow in CCS systems in the future by forecasting the distribution of CO2 gas saturation.