# Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models
## 5 Discussion  
In order to achieve Net Zero Emissions (NZE), it is imperative to prioritize the implementation of safe and effective geological CO\({}_{2}\) storage. In this context, it is necessary to conduct a sensitivity analysis to examine the challenges associated with candidate sites and impose constraints on them while optimizing the process. This study employed SRMs as a replacement for the standard numerical simulation approach in order to reduce computational time while preserving accuracy. The objective was to facilitate rapid investigation and management of CO\({}_{2}\) storage in the Smeaheia saline aquifer. The development of an ANN-based SRM in this study commenced by employing the cascading method. This method involves dividing the simulation time into numerous time steps and training a separate model for each time step. Subsequently, the strategy was altered to a non-cascading way due to the drawbacks associated with the cascading approach. When using a cascade proxy, multiple models need to be trained and optimized for each parameter. This process can be complex and demanding in terms of computational resources. As previously stated, the approach is particularly hindered by error accumulation, which is especially problematic during long-term simulations. To predict 50 years of CO\({}_{2}\) storage, 50 models need to be trained, each with a 1-year time step. MAE was around 1 \(\times\) 10\({}^{-2}\) for training and testing a single CO\({}_{2}\) saturation model, whereas it reached 0.9 for sequential blind evaluation of cascading SRM. Additionally, the developed proxy is intended for an optimization study that needs to predict all models' parameters prior to the desirable time step in the cascading approach. If the proxy models were constructed using the cascade technique, it would require approximately 50 min to sequentially forecast the last time step. Although the cascading method is still faster than numerical simulation (which took 6 h with a dynamic memory allocation of 4000 MB), the non-cascading proxy model simulated each dynamic parameter in only 30 s. It should be mentioned that this study was performed using a computer system with Intel(R) Core (TM) i7-4710 HQ CPU 2.50 GHz and 16 GB RAM. The dataset is influenced by the amount of training scenarios (simulation cases), time step, features, and sampling technique. Multiple time-step durations were examined in order to create the most precise model. No significant improvement was noticed when reducing the time step from 6 months to 3 months. However, by increasing the number of training scenarios, the model was able to accomplish generalization. The only input features excluded are the cell distance from the boundary and the bottom-hole pressure owing to additional computational efforts compared to the model improvement, and lack of well-based SRM, respectively. It is advisable to integrate this model with a well-established SRM in the future in order to incorporate bottom-hole pressure into the grid-based SRM and assess its effects. Besides that, the well placement, injectivity, wellhead pressure, tubing size, as well as perforation length and position can be assessed and optimized. The sampling method depends on the dynamic behavior and distribution of the properties. The pressure model's input was sampled by considering the fluctuation in  
Figure 15: Pressure and CO\({}_{2}\) saturation distribution and error maps after 50 years injection with the rate of 4.683495 \(\times\) 10\({}^{6}\) Sm\({}^{3}\)/day (optimum case), 1 st layer. property values between time steps. This means that only the points near the injection and production wells were chosen, resulting in overfitting and a large inaccuracy for sites located far from the wells. The utilization of random sampling across the reservoir effectively reduced the absolute percentage error of pressure to below 0.5% in the majority of cells by selecting points from various locations.