Research on the ‘Ensemble Smoother’ (ES) method for parameter estimation (history matching) and model conditioning.
Project tasks include:
Evaluation of ES for parameter estimation in a number of known test problems of varying complexity to evaluate ES performance with respect to other traditional methods
Evaluation, implementation and testing of recently proposed iterative smoother algorithms
Development, implementation and testing of new iterative smoother algorithms
Application and demonstration of smoother algorithms in agree field examples.
EmblA is the Nordic Centre of Excellence for ensemble-based data assimilation (DA). It supports Nordic users of DA methods with instruction and training, state-of-the-art open-source codes, and works for R&D in several applications.
Our environment is undergoing major anthropogenic and natural changes that we need to understand, attribute and predict. To be able to use complex 3-dimensional models of our environment to generate reliable predic- tions with documented accuracy is therefore a major challenge for e-Sciences.
