Development of proxy reservoir models for geological carbon storage
The project’s aims are to conduct research on geological carbon storage from the perspective of dynamic analysis and process systems engineering, looking in particular at the dynamics between the wellhead and the CO2 storage reservoir. The main objective is to achieve closed loop operation and management of the reservoir with respect to CO2 sequestration and storage, along with enhanced oil recovery in cases where the reservoir is not fully depleted.
The research aims to develop an integrated approach to the co-optimization of CO2 storage and oil recovery, i.e., closed loop reservoir management. In order to develop closed loop strategies for reservoir management, the elements included in this research include the building of reduced order proxy/surrogate models, experiment design for well placement and parameter estimation, state estimation and model updating using variants of the ensemble Kalman filter. Since reservoir models and compositional simulators are computationally expensive to run, the development of reduced order models are crucial to enable experiment design, state estimation and optimization.
The student’s role in the project will be to develop proxy or surrogate models for reservoir simulators. In the literature, most proxy models have been developed using artificial neural networks (ANNs), with genetic algorithms being also being used in the development of the proxy models. The aim of this project is to use different, potentially better methods to develop reduced order models. The methods to be investigated include Karhunen-Loeve expansion, which is related to principal component analysis. Other methods include the use of suitable reparameterization techniques (e.g., discrete cosine transform, pseudo-parameter grouping) or analytical simplifications (e.g., streamline based models) of the detailed models.