Stein Krogstad
Senior Research Scientist
Stein Krogstad
Senior Research Scientist
Publications and responsibilities
Data-Driven Models Based on Flow Diagnostics
Data-Driven Models Based on Flow Diagnostics
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...
Efficient Adjoint-Based Well-Placement Optimization Using Flow Diagnostics Proxies
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...
Flow diagnostics for model ensembles
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...
User Guide to Flow Diagnostics in MRST - Flow Diagnostics Preprocessors for Model Ensembles
User Guide to Flow Diagnostics Postprocessing - Simulations in MRST and ECLIPSE Output Format
Efficient Adjoint-Based Well-Placement Optimization Using Flow Diagnostics Proxies
Flow Diagnostics For Model Ensembles
Reduced-Physics Multilevel Monte Carlo Methods for Uncertainty Quantification in Complex Reservoirs
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...