| Title | Probabilistic Assessment of the Deep Sedimentary Geothermal System with CO2 Injection at Reservoir Depth |
|---|---|
| Authors | Si-Yong LEE, Feng PAN, Brian MCPHERSON, Joseph MOORE, and Rick ALLIS |
| Year | 2013 |
| Conference | Stanford Geothermal Workshop |
| Keywords | co2, sequestration, geothermal system, response surface method, Monte Carlo simulation |
| Abstract | We have evaluated the potential effect of CO2 as a working fluid for geothermal energy development in the deep sedimentary reservoirs. This study utilizes the response surface methodology for a probabilistic assessment of deep sedimentary geothermal system with CO2 injection. Our approach includes the Box-Behnken design of numerical experiments, 3-D numerical simulations, stepwise regression, and Monte Carlo simulations at each time step. For the numerical experiments, a 5-spot well configuration is simulated within a simplified geologic model consisting of a layered geological setting alternating with unfractured (low k) and fractured limestone (high k) to represent the deep sedimentary geology within the eastern Great Basin. Four independent variables or factors used for this study include pressure drop between injection and production wells, permeabilities of high- and low-k formation, and geothermal gradient. The permeabilities of unfractured and fractured limestone are assumed to be log-normally distributed. Uniform distribution is used for the remaining factors. We assess both the geothermal energy extraction and CO2 storage potential given the range of input parameters. This study demonstrates that the response surface method associated with Monte Carlo simulation can be efficiently applied to the evaluation of a deep sedimentary geothermal system and accompanied CO2 storage within the probabilistic framework. |