Optimization of Relief Well Design Using Artificial Neural Network during Geological CO2 Storage in Pohang Basin, South Korea

This study aims at the development of an artificial neural network (ANN) model to optimize relief well design in Pohang Basin, South Korea. Relief well design in carbon capture and geological storage (CCS) requires complex processes and excessive iterative procedures to obtain optimal operating parameters, such as CO2 injection rate, water production rate, distance between the wells, and pressure at the wells. To generate training and testing datasets for ANN model development, optimization processes for a relief well with various injection scenarios were performed. Training and testing were conducted, where the best iteration and regression were considered based on the calculated coefficient of determination (R-2) and root mean square error (RMSE) values. According to validation with a 20-year injection scenario, which was not included in the training datasets, the model showed great performance with R-2 values of 0.96 or higher for all the output parameters. In addition, the RMSE values for the BHP and the trapping mechanisms were lower than 0.04. Moreover, the location of the relief well was reliably predicted with a distance difference of only 20.1 m. The ANN model can be robust tool to optimize relief well design without a time-consuming reservoir simulations.

Automated summary

The study developed an artificial neural network model to optimize relief well design in Pohang Basin, South Korea. By training and testing with various injection scenarios, the model showed great performance with high R-2 values and low RMSE values, accurately predicting the location of the relief well.