Data-driven optimisation algorithms for local dynamic model adaptivity

Numerical modelling of subsurface flow in the context of gas storage often faces the challenge of long time periods and large spatial domains. Therefore, computational models that are robust, fast and give an accurate prediction of the system are desired, e.g., to help investigate potential gas storage sites, determine optimal operational parameters, and ensure safety of operation. The most efficient model at a specific time during the simulation or at a specific location in the domain can be used by coupling models of different complexity (multiphysics or hybrid model). Resulting multiphysics models are expected to be robust and computationally efficient on domains with varying complexity because they can adaptively match model complexity to domain/process complexity for different times during the simulation and different parts of the domain. The goal of this project is to develop a data-driven optimisation-based adaptive model for gas storage. The model will identify during the simulation run which type of physical model is locally used. The choice of the local model is based on a model hierarchy that reflects the requirements posed by gas storage scenarios. For this purpose, model- and data-based model-selection criteria are developed, the latter being carried out in close collaboration with partners at the TU Delft performing lab- and field-scale experiments.  The model-selection process will be formulated as an optimal control problem that consists of an objective function containing local criteria, while the fulfilment of the model equation appears as constraint. The resulting optimisation problem will be analytically investigated and convergent solution methods will be derived.