Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network

Abstract

This study presents the application of artificial neural network (ANN) to predict storage efficiency of CO2 sequestration in deep saline aquifers. To a create training database used as input and output neurons in ANN, sensitivity analysis of parameters considering the aquifer characteristics was performed by numerical simulation. Based on the analysis, the factors and their ranges influencing CO2 sequestration in deep saline aquifers were determined and 150 representative realizations used as a training database were generated with trapping indices of the residual CO2 and solubility trapping mechanisms. The ANN model was designed with optimum architecture minimizing the mean squared error (MSE) for testing data set and it was tested with validation samples. The results showed that the proposed ANN model had a high prediction performance with a high coefficient of determination (R2) of over 0.99 on comparing with the target values, a low MAPE (mean absolute percentage error) of 1.26%, and RMSE (root mean square error) of 0.41 for TEI (total trapping efficiency index). As a field application, the model has also been evaluated with the field scale data on the Gorae V structure in Block VI-1, Korea. The results of prediction were well matched with the targeted values and accuracy between the ANN predictions and field scale data was achieved with a high coefficient of determination (R2) of more than 0.96, a low MAPE of 4.40%, and RMSE of 3.58 for TEI. From these results, it is believed that the newly developed ANN model can predict storage efficiency with high accuracy and it can also be considered as a useful and robust tool to evaluate the feasibility of CO2 sequestration in deep saline aquifers.