Optimizing carbon capture and storage infrastructure including physics-based reservoir modelling

The deployment of carbon capture and storage (CCS) requires a potentially complex infrastructure to transport and store CO₂ underground. Its optimal roll-out is key to limiting costs and enabling a timely deployment in line with ambitious mitigation scenarios. However, identifying the optimal design of the infrastructure is computationally challenging. Within this framework, mixed-integer linear programming (MILP) offers a computationally effective solution, which, however, requires linear models. Not surprisingly, geological reservoirs are typically represented as static sinks with constant injection rates and storage capacities as parameters. This approach neglects the dynamic properties of CO₂ injection, such as the reservoir pressure evolution over time, limiting the ability to evaluate their impact on the CCS chain design.

In this work, we propose a novel MILP model that integrates physics-based reservoir modelling into CCS chain optimization. Extending existing work on reduced-order modelling of reservoirs, we combine proper orthogonal decomposition and trajectory piecewise linearization to obtain a precise, yet computational efficient MILP model for the dynamic behaviour of CO₂ injection. Compared to full-scale reservoir simulations, the model computes the pressure around the injection well with $\pm$5% accuracy and significant computational speed-ups (500-1800 times faster). We demonstrate its application in a full chain MILP model with an illustrative case study optimizing the decarbonization of a small industrial cluster through CCS, highlighting the model’s ability to couple optimal operation of capture technologies with varying injection rates and to ensure the reservoir safety constraints while designing the chain.