HR: 14:55h
AN: H13I-06    [Abstracts]
TI: Efficient Parameter and State Estimation through Ensemble Kalman Filter and Discrete Cosine Parameterization with Application in Oil Reservoir Characterization
AU: * Jafarpour, B
EM: behnam@mit.edu
AF: Massachusetts Institute of Technology, 48-216 (11), Parsons Lab 77 Massachusetts Ave., Cambridge, MA 02139,
AU: McLaughlin, D
EM: dennism@mit.edu
AF: Massachusetts Institute of Technology, 48-216 (11), Parsons Lab 77 Massachusetts Ave., Cambridge, MA 02139,
AB: State and parameter estimation in large hydrocarbon reservoirs is challenging due to several reasons including: 1) Scarcity of available measurements relative to the number of unknowns, leading to an ill-posed inverse problem; 2) Computational effort required for large reservoir problems; 3) The need to insure that solutions are geologically realistic. All of these problems can be helped by using algorithms that rely on efficient and parsimonious descriptions (or parameterizations) of reservoir properties. This paper combines a novel state and parameter reduction approach, the discrete cosine transform, with a recursive estimation technique, the ensemble Kalman filter, to provide efficient estimation of uncertain states and petrophysical properties (parameters) in large reservoirs. The application and generality of this approach is demonstrated using two waterflooding experiments characterized by different types of geological variability.
DE: 1805 Computational hydrology
DE: 1816 Estimation and forecasting
DE: 1839 Hydrologic scaling
DE: 1846 Model calibration (3333)
DE: 1848 Monitoring networks
SC: Hydrology [H]
MN: 2007 Fall Meeting