CO2 Storage in the North Sea: Quantification of Uncertainties and Error Reduction (CONQUER)

Project Department: Uni Research CIPR (group: Reservoir Simulation) period: 01.01.15 - 31.12.18

About the project

CONQUER is a project funded by the Research Council of Norway (CLIMIT), launched in 2015 and running over four years. CONQUER is based at Uni Research and is a collaboration with the Department of Mathematics at the University of Bergen. The project covers the education of a PhD student in applied mathematics.

The aim is to develop numerical methods to handle uncertainty in simulation of large-scale CO2 storage in aquifers in the North Sea. To  reduce CO2 in the atmosphere, large-scale storage of CO2 in subsurface formations is today a necessary technology to reduce the effect of global warming. The goal is to efficiently store large amounts of CO2, without significant risk for leakage.

The implementation of large-scale CO2 storage requires decisions on injection sites and strategies. When simulations are used to inform the decision making, lack of geological data such as permeability, caprock topography, location of fractures/faults, and stress in the formation will be sources of significant uncertainty. This uncertainty is propagated throughout the storage operation,via capacity estimates, operational constraints and potential creation of leakage. Considering the costs of storage facilities, uncertainty quantification is a key enabling technology for realizing large-scale storage.

The bottleneck is the lack of reliable tools for quantifying uncertainty due to limited data. In this project the knowledge gaps will be bridged by large-scale simulation tools for uncertainty quantification. We focus on three key processes that may lead to storage constraints: pressure buildup during injection, long-term migration of CO2, and leakage through the caprock overlying the formation. We devise a model based on interconnected modules, each consisting of a tailored numerical method for one of the problems (geomechanics model, pressure problem and transport problem). Due to the prohibitive numerical cost of a direct implementation (complex physics and many sources of uncertainty), we use reduced order models and simplified physics. The expected outcome is a setof open source simulation tools where the errors from lack of data, modeling errors and numerical errors are systematically treated within a unified framework.

External research partners: University of Colorado, Boulder, CO, USA, and ETH Zurich, Switzerland


cp: 2019-12-04 11:16:16