Geologic CO2 sequestration in deep saline aquifers is a promising technique to mitigate the effect of greenhouse gas emissions. Designing optimal CO2 injection strategy becomes a challenging problem in the presence of geological uncertainty. We propose a surrogate assisted optimisation technique for robust optimisation of CO2 injection strategies. The surrogate is built using Adaptive Sparse Grid Interpolation (ASGI) to accelerate the optimisation of CO2 injection rates. The surrogate model is adaptively built with different numbers of evaluation points (simulation runs) in different dimensions to allow automatic refinement in the dimension where added resolution is needed. This technique is referred to as dimensional adaptivity and provides a good balance between the accuracy of the surrogate model and the number of simulation runs to save computational costs. For a robust design, we propose a utility function which comprises the statistical moment of the objective function. Numerical testing of the proposed approach applied to benchmark functions and reservoir models shows the efficiency of the method for the robust optimisation of CO2 injection strategies under geological uncertainty.
- Adaptive sparse grid interpolation (ASGI)
- CO<inf>2</inf> sequestration
- Geological uncertainty
- Robust optimisation
- Surrogate-assisted optimisation
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Computers in Earth Sciences
- Computational Mathematics
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- School of Energy, Geoscience, Infrastructure and Society - Professor
- School of Energy, Geoscience, Infrastructure and Society, Institute for GeoEnergy Engineering - Professor
Person: Academic (Research & Teaching)