## 7 Conclusions  
An artificial neural network (ANN) model has been developed for an optimal relief well design, using information such as the location and operating conditions, which maximize CO\({}_{2}\) storage capacity in Pohang Basin, South Korea. Based on the observations, the following conclusions have been drawn:  
1. Based on the comparisons made for with or without relief well cases, the average injection rate was 3 tons/day higher if a relief well is operated. In addition, it was found that the relief well extended the injection period by 0.99 years and increased the CO\({}_{2}\) storage capacity by 19.44% (215 kton).
2. To generate training datasets for the input and output nodes in the ANN model, the operating conditions of both wells and the location of the relief well were optimized to achieve the maximum cumulative mass of the injected CO\({}_{2}\). It was found that the cumulative mass for the 10-, 20-, and 30-year injection periods were 218, 292, and 332 kton, respectively. Therefore, the cumulative injection mass increased by 33.94% (74 kton) and 13.7% (40 kton) as the injection period was extended from 10 to 20 years and from 20 to 30 years, respectively. Consequently, it was concluded that 20 years of injection with the relief well would be the best scenario in terms of safe and effective storage in Pohang Basin.
3. The ANN model was developed with datasets of 10- and 30-year injection scenarios and validated with that of the 20-year scenario. The optimal architecture of the model consisted of 63 nodes and seven hidden layers at 209 iterations. When the predicted data were compared to the validation data, the ANN model reliably predicted the result with an \(R^{2}\) of 0.9982 and RMSE of 0.6681 for the CO\({}_{2}\) injection rate, and an \(R^{2}\) of 0.9828 and RMSE of 0.0497 for the injection well BHP. In addition, the developed ANN model had great accuracy in the prediction of the trapping indices, with an \(R^{2}\) of 0.9927 and RMSE of 0.0136 for the RTI, and an \(R^{2}\) of 0.9607 and RMSE of 0.0085 for the STI, respectively. The total CO\({}_{2}\) storage capacity and the relief well location were also accurately predicted with only a 0.68% difference (2 kton) and a distance of 20.1 m, respectively.  
Consequently, it was concluded that the developed ANN model can accurately optimize relief well design and evaluate storage efficiency in Pohang Basin, South Korea. In addition, the ANN model can be a robust tool to optimize relief well design and to evaluate storage efficiency for successful CO\({}_{2}\) sequestration without a time-consuming reservoir simulation at the early stages of a CCS project.  
Conceptualization, Y.S. and J.W.; funding acquisition, J.W.; methodology, Y.S.; project administration, J.W.; resources, J.W.; software, Y.S.; supervision, J.W.; writing--original draft, Y.S.; writing--review & editing, J.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Agency for Infrastructure Technology Advancement grant funded by Ministry of Land, Infrastructure and Transport (21 IFIP-B160208-05).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.