Lawrence Berkeley National Laboratory

Closed-loop heat exchange for geothermal energy production involves injecting working fluid down a well that extends through the geothermal resource over a significant length to absorb heat by conduction through the well pipe. The well then needs to return to the surface for energy recovery and fluid re-injection to complete the cycle. We have carried out mixed convective-conductive fluid-flow modeling using a wellbore flow model for TOUGH2 called T2Well to investigate the critical factors that control closed-loop geothermal energy recovery. T2Well solves a mixed explicit-implicit set of momentum equations for flow in the pipe with full coupling to the implicit three-dimensional integral finite difference equations for Darcy flow in the porous medium. T2Well has the option of modeling conductive heat flow from the porous medium to the pipe by means of a semi-analytical solution which makes the computation very efficient because the porous medium does not have to be discretized. When the fully three-dimensional option is chosen, the porous medium is discretized and heat flow to the pipe is by conduction and convection, depending on reservoir permeability and other factors. Simulations of the closed-loop system for a variety of parameter values have been carried out to elucidate the heat recovery process. To the extent that convection may occur to aid in heat delivery to the pipe, the permeability of the geothermal reservoir, whether natural or stimulated, is an important property in heat extraction. The injection temperature and flow rate of the working fluid strongly control the ultimate energy recovery. Pipe diameter also plays a strong role in heat extraction, but is correlated with flow rate. Similarly, the choice of working fluid plays an important role, with water showing better heat extraction than CO2 for certain flow rates, while the CO2 has higher pressure at the production wellhead which can aid in surface energy recovery. In general, we find complex interactions between the critical factors that will require advanced computational approaches to fully optimize.