Optimal design of experiments in 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 main thrust areas of the project are described below.
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 work on the development of strategies for the optimal design of experiments in geological carbon storage. There are two main aspects to this work: the first is the use of surrogate / proxy reservoir models for D optimal experiment design, and comparison of the results with Bayesian sequential experiment design. The second aspect is to obtain optimal subsets or groups of parameters that are identifiable from the experiment design.This project will involve the development and modification of MATLAB code for design of experiments and reparameterization. Code has already been developed in the group for these techniques, and the student’s main role will be to modify the code for use with reservoir simulations, and analysis of the results.
