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Stein Krogstad

Senior Research Scientist

Stein Krogstad

Senior Research Scientist

Stein Krogstad
Phone: 918 34 023
Email:
Department: Mathematics and Cybernetics
Office: Oslo

Publications and responsibilities

Publication
https://www.sintef.no/en/publications/publication/1849901/

Ensembles of geomodels provide an opportunity to investigate the range of parameters and possible operational outcomes for a reservoir of interest. Full-featured dynamic modelling of all ensemble members is often computationally unfeasible, however some form of dynamic modelling, allowing us to...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/1849921/

Optimizing placement and trajectory of wells is a computationally demanding, and hence time-consuming task due to the high number of simulations typically required to achieve a local optimum. In this work, we combine three remedies for speeding up the workflow; firstly, we employ a flow-diagnostics...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/1849924/

Data-driven models are an attractive alternative to reservoir simulation in workflows where full field-scale simulations may be computationally prohibitive [3,4]. One example is the forecasting and schedule optimization of waterflooding scenarios, where numerous function evaluations that correspond...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/1831076/

The Monte Carlo (MC) method is an appealing candidate for uncertainty quantification in reservoir simulation for three reasons: (i) It is the preferred approach for systematic reduction in variance for cases with high-dimensional uncertainty with a strongly nonlinear effect (robustness); (ii) it is...

Year 2020
Type Academic lecture