HR: 09:00h
AN: H11E-05    [Abstracts]
TI: Efficient Uncertainty Quantification and Model Updating of Subsurface Flow Models
AU: * Zhang, D
EM: donzhang@ou.edu
AF: The University of Oklahoma, Mewbourne School of Petroleum and Geological Engineering, SEC T301, 100 E. Boyd, Norman, OK 73019 United States
AU: Lu, Z
EM: zhiming@lanl.gov
AF: Los Alamos National Laboratory, Earth and Environmental Division, Los Alamos, NM 87545 United States
AU: Chen, Y
EM: yan@ou.edu
AF: The University of Oklahoma, Mewbourne School of Petroleum and Geological Engineering, SEC T301, 100 E. Boyd, Norman, OK 73019 United States
AB: We present an efficient, accurate approach for quantifying uncertainties associated with subsurface flow simulations and for continuously updating the flow models on the basis of dynamic data. This approach is relied on the principle modes of the underlying random fields obtained via the Karhunen-Lo"Ýve (KL) decomposition. In the approach, the covariance of the formation properties is approximated by a small set of eigenvalues and eigenfunctions using the KL decomposition, and reconstruction of this covariance from the KL decomposition can be done whenever needed. In each update, the forward problem is solved using the KL-based moment method, giving a set of functions from which the mean and covariance of the state variables can be constructed, when needed. The statistics of both the formation properties and the formation responses are then updated with the available measurements at this time using the auto- and cross-covariances obtained from the forward problem. This approach for both forward and inverse problems is illustrated on heterogeneous formations with dynamic measurements and the results are compared with those from other methods, in terms of accuracy and efficiency.
DE: 0500 COMPUTATIONAL GEOPHYSICS (3200, 3252, 7833)
DE: 1800 HYDROLOGY
DE: 1829 Groundwater hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: Fall Meeting 2005