Ensemble Kalman Filter for estimation of rock properties in geothermal reservoirs characterized by fractured rocks or fluviatile sediments

• Applicants: Professor Dr. Christoph Clauser; Professor Dr. Harrie-Jan Hendricks-Franssen
• Subject Area: Geophysics ; Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Fluid Mechanics
• Term: from 2013 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 238370553

The rock properties controlling hydrothermal transport are interesting in many studies of groundwater flow, but are in particular important for the assessment of geothermal reservoirs. The topic of this project is to estimate these properties using the Ensemble Kalman Filter Method (EnKF). The related physical model for porous flow is numerically provided by the in-house simulator SHEMAT-Suite. As most inversion methods, the EnKF in its original form, is based on a Gaussian distribution of the parameters to be estimated. However, the crucial rock parameter for hydrothermal fluid flow is permeability which often has a bi-modal distribution in a reservoir. This may be caused by a superposition of porous and fractured (natural or engineered) permeability or intercalations of fluvial deposits in a sedimentary reservoir. Similar bi-model distribution can be observed for thermal conductivity in lithologically heterogeneous reservoirs. For reservoir analysis, the EnKF method has the decisive advantage that jointly with a parameter estimate it provides also a measure of the error of the estimate (variance). However, small ensembles tend to underestimate this error. In the proposed project we intend to improve the EnKF method for hydrothermal parameter estimation by implementing various new methods and study their single and combined effects: (1) Normal Score EnKF for transformation between normal and bi-modal parameter distributions, (2) localizations methods, to weight the filter function depending on the distance to the observation points, (3) a method for conditioning the covariance matrix (covariance inflation) to reduce the underestimation of the variance in EnKF. Here we will extend existing methods for parameter estimation and develop new approaches, in particular for localization. Once these methods are implemented, they will be tested on synthetic reservoir models (1) for a crystalline EGS reservoir and for a sedimentary reservoir containing fluvial deposits, and (2) on time series of chemical tracer concentration in two wells of the Soultz-sous-Forêts geothermal test site.

• DFG Programme: Research Grants
• Participating Person: Privatdozentin Dr. Gabriele Marquart