Surrogate-assisted reservoir history matching

In the community of petroleum engineering, the use of surrogate modelling techniques have recently gained more and more popularity to improve the efficiency of history matching. However, it is still not possible to fully utilize their potential in realistic applications. One of the challenges is to retain high accuracy while increasing the...

A Local Particle Filter Using Gamma Test Theory for High-Dimensional State Spaces

Particle filters are non-Gaussian filters, which means that the assumption that the error distribution of the ensemble should be Gaussian is unnecessary. Like the ensemble Kalman filter, particle filters are based on the Monte Carlo approximation to represent the distribution of model states. It requires a substantial number of particles to...

OpenDA-NEMO framework for ocean data assimilation

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently...

Improving the Algal Bloom Prediction in the North Sea by Ensemble Kalman Filtering in the GEM/BLOOM Model

The ecological state of the North Sea surface water can be indicated by ocean variables such as the Chlorophyll-a (Chlfa) concentration. Chlfa is the principal photosynthetic pigment and is common to all phytoplankton and can therefore be used as a measure of phytoplankton biomass. The GEM/BLOOM model developed at Deltares is a generic...