Closed loop reservoir management for 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 student’s role in the project will be to work on the development of a strategy for closed loop management (i.e., control) of geological carbon storage. The main aspect of this work will be the development of a CO2 injection strategy based on a given reservoir model, and then integrating the CO2 injection with the updating of the model and a monitoring / measurement scheme to provide closed loop feedback to the injection. The objective is to specify production-related variables such as wellhead pressures and phase rates, while interpreting sensor information that may be available from well tests, seismic monitoring or other surveillance data and transforming it to a form usable by the control algorithm. The primary objective is to specify methods for the optimal operation of smart wells. Smart wells are unconventional wells with downhole instrumentation (including sensors, valves and inflow control devices) installed in the production tubing. These wells offer continuous in-situ monitoring of fluid flow rates and pressures, and the periodic adjustment of downhole valves. Another aspect that the student can investigate is whether cyclic or periodic patterns in the input flow and pressure signals can improve CO2 injectivity.

This project will involve the development of MATLAB code for closed loop control. Data for the control algorithm will be generated using reservoir simulations. In addition to the Professor, graduate students with expertise in model predictive control will be able to provide guidance on the development of the code.

Faculty Supervisor:

Dr. Stevan Dubljevic


Prasanjeet Poddar (waitlist)



Engineering - chemical / biological


Oil and gas


University of Alberta


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