Trond Mannseth

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In the management of reservoirs it is an important issue to utilize the available data in order to make accurate forecasts. In this paper a novel approach for frequent updating of the near-well reservoir model as new measurements becomes available is presented. The main focus of this approach is to have an updated model usable for forecasting. These(More)
Uncertainty assessment for parameter estimation problems is considered. It is customary to use confidence regions or intervals to give the precision of the parameter estimates. The method most used, because of modest computational requirements, is linearized covariance analysis. Monte Carlo analysis can be used to check the validity of the linearization for(More)
We propose a solution strategy for parameter estimation, where we combine adaptive multiscale estimation (AME) and level-set estimation (LSE). The approach is applied to the nonlinear inverse problem of recovering a coefficient function in a system of differential equations from spatially sparsely distributed measurement data. The specific equations(More)
We consider numerical identification of the piecewise constant permeability function in a nonlinear parabolic equation, with the augmented Lagrangian method. By studying this problem, we aim at also gaining some insight into the potential ability of the augmented Lagrangian method to handle permeability estimation within the full two-phase porous-media flow(More)
The ensemble Kalman filter (EnKF) is an ensemble-based Monte Carlo formulation of the Kalman filter. In most practical cases it is based on a low-rank approximation of a covariance matrix from a moderately sized ensemble. Sampling errors lead to artificial effects, such as spurious correlations, deteriorating the estimates and the forecasts of the system(More)