# Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models
## 1 Introduction
In addition, the training of intricate SRMs necessitates a substantial number of numerical simulation cases [47]. The Smeaheia storage project, which is part of the Northern Lights' full-scale CCS project, has been granted a license [48]. The project aims to develop transport and storage solutions for up to 20 million tons of CO\({}_{2}\) per year (to be operational by 2028) and covers a large area east of the Troll field off the west coast of Norway. Smeaheia possesses an extensive numerical model, developed by [PERSON] and [PERSON] [49] that is computationally demanding to conduct long-term simulations. Many simulation runs are necessary to evaluate and optimize the development plan of this project. Additionally, no proxy models have been developed for this aquifer model to reduce the computing costs. This computational challenge will be more significant considering the ambition of 30-50 million tons per annum capacity for CO\({}_{2}\) transport and storage by 2035, by storing all the transported CO\({}_{2}\) in Smeaheia and other potential storage sites as they are matured, thus making the models bigger and more complex. The objective of this study is to create the first SRM for the Smeaheia aquifer and enhance the efficiency of long-term CO\({}_{2}\) storage simulations. The potential risks associated with this storage project are the migration of CO\({}_{2}\) plume towards the leakage points and the fracturing of the caprock [50; 51; 52]. Hence, the objective of optimization is to maximize the storage of CO\({}_{2}\) while simultaneously imposing restrictions on the CO\({}_{2}\) saturation and cell pressure below the caprock. This study shall utilize grid-based SRMs to forecast the pressure and CO\({}_{2}\) saturation within the cells in a computationally effective way. SRMs will be created for this real-world reservoir model that consists of about 1.5 million grid cells and spans 50 years. Although RNNs can anticipate intermediate time steps and the period after injection, they also result in higher computational expenses for both training and computation. Furthermore, the training of CNNs requires a large number of numerical simulation examples, as demonstrated by [PERSON] et al. [41], who utilized 200 simulation cases. Given the extensive simulation time required for a complex model like Smeaheia, it is not practical to conduct such a large number of simulation cases. Furthermore, it would be impractical to create a proxy model for optimization. Hence, the DNN approach will be employed for the development of SRMs. In this technology, the prediction of features for each grid cell can be performed independently of other cells. This allows for the simulation volume to be targeted specifically to the desired cells and reduces the time required for optimization. This study (which is mainly based on the results obtained in [53]), will provide a concise introduction to ANN and GA in the second section. Subsequently, the methodology will involve investigating the features of the Smeaheia aquifer and updating its numerical model. This will be carried out by incorporating pore volume multipliers and creating an injection well. The updated model will be utilized for generating training and validation data for two grid-based SRMs that forecast pressure and CO\({}_{2}\) saturation. Once the model has been trained and validated without previous knowledge of the data, it will be employed to optimize CO\({}_{2}\) storage. Ultimately, the outcome of the proxy-based optimization will be verified against numerical simulation results. After presenting the results, the discussion part offers an analysis of the results and comprehensively elucidates all the limitations and challenges faced in constructing the ultimate model.