AbstractAn Artificial Neural Network surrogate modelling approach was used to optimise CO2 storage into a highly heterogeneous semi- closed saline aquifer which exhibits considerable pressure increase due to injection. The methodology was implemented to minimise the overall field pressure and well bottom-hole pressures, and to maximise the amount of dissolved and trapped CO2 in the storage aquifer. Different realisations of permeability and porosity were stochastically generated to represent the uncertainty in the model. Artificial neural networks were used to reduce the computational time of the optimisation procedure by approximating the objective functions for CO2 storage as surrogates to the expensive solutions of flow by the simulator. A multi- objective evolutionary algorithm was run on these approximators to generate solutions of the multi-objective optimisation's Pareto front. These solutions were compared with the solutions obtained by the computationally expensive optimisation and they were found to give satisfactory results, illustrating that this methodology can be a viable, and low computational cost alternative for optimisation in CO2 storage design.