Jan Dirk Jansen

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Description: This workshop will assess the current state-of-the-art and identify needs and opportunities for future research at the intersection of large-scale inverse problems and uncertainty quantification. It will bring together and cross-fertilize the perspectives of researchers in the areas of inverse problems and data assimilation, statistics,(More)
The past ten years have seen an increasing application of systems and control theory to porous media flow. This involves in particular the use of optimization, parameter identification, and model reduction techniques in attempts to increase the amount of oil or gas that can be recovered from subsurface hydrocarbon reservoirs. Other applications involve the(More)
— Studies on dynamic real-time optimization (D-RTO) of waterflooding strategies in petroleum reservoirs have demonstrated that there exists a large potential to improve economic performance in oil recovery. Unfortunately, the used large-scale, nonlinear, physics-based reservoir models suffer from vast parametric uncertainty and generally poor short-term(More)
Model-based economic optimization of oil production suffers from high levels of uncertainty. The limited knowledge of reservoir model parameters and varying economic conditions are the main contributors of uncertainty. The negative impact of these uncertainties on production strategy increases and becomes profound with time. In this work, a multi-objective(More)
In this paper, a novel framework for reduced order modeling in reservoir engineering is introduced, where tensor decompositions and representations of flow profiles are used to characterize empirical features of flow simulations. The concept of classical Galerkin projection is extended to perform projections of flow equations onto empirical tensor(More)
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