In this short course, we cover a modern approach to managing and modelling uncertainty in subsurface formations within a decision making framework. We outline an uncertainty quantification workflow that focuses on several elements:
Decision-driven sensitivity analysis to determine key reservoir variables
Geological scenario development to avoid reducing uncertainty artificially and during history matching
Use of seismic and production data for model rejection
Issues of computational challenges through model validation and screening
Course Level: Intermediate Instructor: Jef Caers & Marco Thiele
Designed for you, if you are...
A reservoir geologist, geophysicist or engineer who is involved in a multi-disciplinary asset team building uncertainty models for reservoir appraisal and production planning
How we build your confidence
The course uses practical field studies to guide you through the modelling workflow from geological interpretation to history matching and forecasting
In addition to the course manual you will also receive the textbook 'Modeling Uncertainty in the Earth Sciences' by Jef Caers
By the end of the course, you will feel confident in your understanding and use of practical workflows for modelling uncertainty and the integration of geological, geophysical and production data for forecasting and decision making.
What is uncertainty?
Managing uncertainty in the oil & gas industry
Decision making under uncertainty
Representing uncertainty in metric space
Decision-focused sensitivity analysis for reservoir models
Validating uncertainty models with reservoir log, seismic and production data
Model selection and model complexity: addressing the computational challenge
Uncertainty quantification with seismic and production data
"It opens a new area in the industry and changes your view on it" - Reservoir Engineer at MND "Very thought provoking, excellent tools and strategies for problem solving." - Reservoir Engineer at BG Group