Uncertainty quantification is a synthesis course that brings together various disciplines such as geology, geophysics, reservoir engineering, data science and decision analysis. Uncertainty quantification is not seen as some posterior analysis, or skill, but as key to successful decision making in real field situations. Participants will learn how a proper management of uncertainty reduces costs and unwanted surprises.
In this short course we cover a modern approach to managing and modelling uncertainty in subsurface formations within a decision making framework. The approach is based on a new book 'Quantifying Uncertainty in Subsurface systems (Wiley 2018)', and a new protocol for uncertainty quantification termed Bayesian evidential learning. Several elements of this protocol are:
- Decision making under uncertainty using decision science
- Development of prior model uncertainty and Monte Carlo
- Falsification of model uncertainty using reservoir data
- Strategies for uncertainty reduction that avoid complex and time-consuming history matching
Course Level: Skill
Duration: 3 days
Instructor: Jef Caers