The project is about the development of efficient phase and chemical equilibrium algorithms and the implementation of them into CO2 storage simulation. The RAND-based algorithms will be used to improve efficiency and robustness compared to existing simulators.
In the CompReact project funded under the INNO-CCUS program, we plan to develop a next-generation compositional CO2 storage simulator with multiphase geochemical reactions.
The simulator will be applicable to a wide range of geological reservoir types (aquifers and depleted petroleum reservoirs) and have dramatically improved robustness and efficiency compared to existing simulators.
This will be achieved by utilizing the novel RAND-based multiphase reaction algorithms recently developed at DTU. The RAND algorithms will provide new formalism for compositional CO2 storage simulation with multiphase reactions.
Furthermore, in collaboration with Stanford University, we will couple our algorithm with the GEOS simulator developed by Stanford University, LLNL (Lawrence Livermore National Laboratory), and TotalEnergies.
In this PhD project under CompReact, the focus will be on the development of a highly efficient compositional reactive simulator using the RAND-based algorithms. The algorithms are minimization-based and guaranteed to converge.
They give a smaller set of equations for multiple phases and a second-order convergence rate, thus highly efficient. The algorithms treat phase and chemical equilibrium simultaneously, avoiding the conventional sequential solution.
They are especially suited to situations with many phases and reactions, like CO2 storage with many speciation and mineral precipitation reactions. The current multiphase geochemical equilibrium module will be extended by including kinetics and a database for relevant geochemical reactions.
The module will be further improved with its code efficiency and reliability, and then integrated into an in-house compositional simulator for analysis of injection and storage problems at relatively short time scales.
The experience accumulated in this PhD project will contribute to the further development of GEOS in the parallel PhD projects.
Main supervisor: Wei Yan
Co- supervisor: Erling H. Stenby