GeoStim: Mathematical modeling and simulation of fracture opening in geothermal reservoirs
The GeoStim project combines mathematical modeling, discretization techniques and geo-mechanics to develop reliable and tailored simulation tools for opening of fractures, with the aim of increasing the potential for geothermal energy mining.
Although geothermal energy has great potential as a source of renewable energy, current implementations of the technology is motsly limited to high temperature locations near techtonic zones, or to the production of energy for heating. To realize the full potential of geothermal energy, enhanced systems, where the properties of the energy storing rock is altered, must be considered. One such technique is low-pressure stimulation, where fractures already existing in the rock are stimulated (opened) by injecting fluids to improve the flow conductivity of the rock.
The fracture opening is goverened by a combination of fluid flow through the porous rock, and the geo-mechanical response to the pressure increase, including the fracturing. To simulate this process, the project will
- Develop a mathematical and numerical framework to simulate opening of discrete fractures due to shear dilation in a low-pressure stimulation scenario.
- Synthesize up-scaled models of the dynamical coupling between the fluid pressure and the fracture network.
- Utilize a multiscale framework to dynamically identify the correct level of upscaling of fracture families at different stages of the stimulation and production process.
- Propose design principles for efficient simulation tools for the hydraulic shearing process by considering schemes for the coupling of the physical processes involved.
In the project, both mathematical models and numerical discretization schemes for the fracturing process are deviced. The project thus relies heavily on both the formulation of physics in terms of equations, analysis of the equations, and the implementation of solution approaches using advanced numerical methods.
Inga Berre: Project leader, associate professor
Eirik Keilegavlen: Researcher
Jan Nordbotten: Professor
Eren Ucar: PhD student
Michael Sargado: PhD student