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Modelling and Managing Uncertainty in the Subsurface (RES38)


    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 Duration: 3 days
    Course Level: Skill
    Instructor: Jef Caers

    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 'Quantifying Uncertainty in Subsurface Systems’ by Jef Caers

    The benefits from attending

    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.


    • Bayesianism: What is uncertainty?
    • Managing uncertainty in the oil & gas industry
    • Decision making under uncertainty using decision science
    • Monte Carlo & falsification
    • Global sensitivity analysis
    • Model selection and model complexity: addressing the computational challenge
    • Uncertainty quantification with seismic and production data
    • Calculating value of information

    Customer Feedback

    "Good material, well presented. Case studies were the highlight." - Petroleum Engineer at Aker

    "The topic is difficult, but Jef was phantastic in bringing things down to the simplest examples." - Sr. Reservoir Engineer at OMV

    "The topic is widely useable, not only to surface modelling. Jef very intelligently brings all facets together. Important for the RE & Modelling crowd to follow this approach. I believe we need a Part II of this course." - Production Engineer at OMV

    "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




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