The SPE11 comparative solution project presents a benchmark for geological carbon storage in an aquifer, as the development of sufficiently accurate CO₂ sequestration models is critical for predicting the distribution of CO₂ during and after injection. In this paper we present a convergence analysis of the SPE11 benchmark simulation using the Delft Advanced Research Terra Simulator (open-DARTS). Open-DARTS, an open-source simulation framework designed for forward and inverse modeling, as well as uncertainty quantification, employs a unified thermal-compositional formulation and operator-based linearization. In our convergence analysis the SPE11b (2D - reservoir conditions) starts to converge at a grid resolution of 1340 × 240, after which added resolution provides diminishing returns. In addition the three-dimensional SPE11c benchmark is simulated with 8M grid blocks. However, 2D results from SPE11b suggest that a greater resolution is required for a truly converged solution. Furthermore, we extend the SPE11b benchmark to include H₂S as a trace impurity in the injection stream.

Carbon capture and storage is an essential technology to mitigate anthropogenic CO2 emissions from carbon-intensive industries. To model CO2 injection, physics-based numerical methods are computationally intensive due to the nonlinear nature of the governing equations. Therefore, several data-driven deep learning methods have been developed to serve as proxies and replace numerical simulations. These proxies have demonstrated significantly faster runtimes while maintaining comparable accuracy to numerical simulations. This makes them suitable for high-fidelity models and ensemble-based techniques that require a large number of forward runs. Our method utilizes physics-informed neural networks (PINNs) to parameterize the solution space of immiscible compositional problems. The PINN parameterizes the forward solution of the compositional problem based on the composition of the upstream grid block at the updated time step, the composition of the current grid block at the current time step and the total velocity at their interface. The neural network is trained in the entire solution space and is used in a sequential, cascading solver. In this approach, we obtain the pressure solution first before solving for transport by treating the reservoir as a series of two-cell problems. The resulting transport solver is applicable to all problems with different initial/injection conditions and different heterogeneous reservoirs. We demonstrate our approach for binary and multicomponent problems and furthermore use multilinear interpolation to compare and validate the solution method.