Monitoring of pressure build-up can provide explicit information on reservoir integrity. This work evaluated pressurization of a CO2 reservoir system in the presence of leakage pathways as well as exploring the effects of compartmentalization of the reservoir utilizing metamodeling techniques (e.g. Design of Experiments (DoE) and Response Surface Methodology (RSM)). Two simulation models were developed (1) an idealized scenario for the evaluation of multiple DoE methods, and (2) a complex scenario implementing the best performing DoE method to investigate pressurization of the reservoir system. The evaluation of the idealized scenario determined that the Central Composite design would be suitable for the complex scenario. The complex scenario evaluated 5 uncertain factors such as the permeabilities of the reservoir, seal, leakage pathway and fault, and finally the location of the pathway. A total of 36 response surface equations (RSEs) were developed for the complex scenario with a coefficient of determination (R2) of 0.95 and a Normalized Root Mean Square Error (NRMSE) of 0.060. Sensitivities of RSEs to the input factors were dynamic through space and time. At the earliest time, the impact of the reservoir permeability was dominant, whereas the fault permeability became dominant for later times (>0.5 years). The RSEs were implemented in a Monte Carlo Analysis to analyze leakage and pressurization risks. At the earliest time, the permeability of the leakage pathway had a sufficiently high influence on the above-zone pressure (i.e., the change in pressure in an aquifer overlying the sealing formation) allowing for adequate determination of leakage risk. At later times, the fault permeability became dominate inhibiting the determination of leakage risk while allowing for sufficient determination of pressurization risk.
Bibliographical noteFunding Information:
This research was partly funded by both R&D Project on Environmental Management of Geologic CO2 Storage offered by the Korea Environmental Industry & Technology Institute (Project Number: 201400180004) and new faculty research equipment support grant from Yonsei University. The authors also appreciate partial financial support by the Advanced Opportunity Program Fellowship provided by the University of Wisconsin-Milwaukee.
© 2016 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Management, Monitoring, Policy and Law
- Industrial and Manufacturing Engineering