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Reservoir Modelling Workflows (GEO20)


    Constructing a reservoir model includes the difficult task of integrating data from very different sources like seismic, well, core and wireline information as well as sedimentological concepts and facies interpretations. This course will show how modern reservoir modelling practice handles such different viewpoints from geoscience and engineering.
    The course starts with characterising reservoirs in terms of structure, sedimentology and related petrophysical properties and develops these into reservoir models. Geostatistics and data integration across the key disciplines will set the baseline.
    This training will teach state-of-the-art concepts, practical fundamentals and common pitfalls when using applications in integrated computer-based modelling. It aims to cover deterministic and stochastic techniques in reservoir modelling and shows how to apply these for populating facies, and properties like porosity, permeability or saturation. The practical character will be emphasized by demonstrating best practices for constructing useful simulation models.

    Course Level: Skill
    Instructor: Wilfried Gruber

    Designed for you, if you are...

    • A geologist, geophysicist or petroleum engineer seeking to gain an understanding and practical knowledge in reservoir characterisation, geostatistics and modelling

    How we build your confidence

    Theory in statistics, geostatistics, interpolation and simulation techniques will be communicated to provide a firm knowledge base. The course is designed in a way that each main topic will consist of a theory section followed by a computer show case (demo) for application of the learned and opportunities for in depth discussions.

    The benefits from attending

    At the end of the course, participants will have confidence on how to characterize, model and manage reservoirs using geostatistics. The key points to take will be:

    • Showing the methods and benefits of integrating geological, geophysical, petrophysical and engineering data into reservoir models
    • Introducing state-of-the-art deterministic and stochastic modelling techniques, demonstrating their application and outcomes
    • Gaining skills on making realistic assumptions of reservoir parameters and the associated spread of model uncertainties
    • Discussing the full workflow from data input and analysis through modelling and upscaling results into a model ready for flow simulation


    • Introduction to reservoir characterisation
    • Reservoir metrics and related variable types
    • The setup of a reservoir model
    • Grid design and structure determination
    • Structure model show case
    • Geostatistics and spatial data analysis
    • Statistics show case
    • Variograms, Kriging basics and methods, Co-kriging
    • Kriging show case
    • Deterministic and stochastic property modeling
    • Simulation methods for discrete variables (indicator simulation, TGS, Boolean, MPS, etc.)
    • Methods for and continuous variables (SGS, GRFS, bivariate, etc.)
    • Show case on facies models and simulation
    • Modeling strategies
    • Reservoir modeling show case
    • Volume estimates and uncertainty assessment
    • Grid upscaling to the simulation model
    • Show case




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