###### Abstract  
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 CO\({}_{2}\) 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. pore pressure build-up occurs, which may lead to caprock instability, fault reactivation, and possible facility damages [14; 17]. Although multiple methods have been proposed to improve CO\({}_{2}\) injectivity, such as acidizing, fracturing, clay fixation, and thermal treatment, no significant enhancement has been reported yet [18; 19; 20]. Installing a \"relief well\" may solve problems related to pore pressure build-up by relieving it by producing formation fluid during CO\({}_{2}\) injection [21; 22; 23]. There are studies using numerical approaches focusing on operating a relief well in a deep saline aquifer to improve both the CO\({}_{2}\) injectivity and the total storage capacity. [PERSON] et al. [24] proposed the active CO\({}_{2}\) reservoir management (ACRM) strategy, which combines water production with CO\({}_{2}\) injection to mitigate pore pressure build-up and enhance the CO\({}_{2}\) injection rate. The authors insisted that producing formation water can provide safer CO\({}_{2}\) storage by relieving pore pressure. [PERSON] et al. [25] proposed that a produced water well can be repurposed for CO\({}_{2}\) injection after CO\({}_{2}\) breakthrough. [PERSON] et al. [26] developed the constrained differential evolution (CDE) algorithm to solve global optimization problems related to pressure management in geological CO\({}_{2}\) storage, targeting a sandstone reservoir in the Southern San Joaquin Basin in California. [PERSON] et al. [27] analyzed CO\({}_{2}\) storage capacity using a water production well from a saline aquifer. [PERSON] et al. [28] proposed dual-tubing, which combines a CO\({}_{2}\) injection well and a horizontal water production well, to improve CO\({}_{2}\) storage capacity. Although adopting numerical approaches may be a reliable method for a relief well design, it requires complex processes and excessive iterative procedures to obtain the optimal operation variables, such as CO\({}_{2}\) injection rate, water production rate, the distance between the wells, and pressure at the relief well. Numerous case studies need to be performed once a relief well location is taken into account for optimization. These challenges can be solved by implementing an artificial neural network (ANN), which is one of the most frequently used artificial intelligence methods for geo-scientific problems, by identifying the non-linear relationship between parameters by learning patterns [29]. The ANN has been incorporated into problems related to reservoir characterization, multiphase fluid flow, hydrodynamic modeling, hydraulic fracturing fluid leak-off prediction, fluid production, and injection design [30; 31; 32; 33; 34; 35; 36]. For CCS designs, [PERSON] et al.