APPLIED GEO-STATISTICS FOR RESERVOIR MODELING AND CHARACTERIZATION

  • June 15, 2020 - June 19, 2020
    8:00 am - 5:00 pm

Course Length:   5 Days        Course Dates:    June 17 – 21, 2019     Course Venue:  Calgary, Alberta, Canada

 

Course Description:

This course addresses the application of geo-statistical techniques to build reservoir models through the integration of geological, core/well log, seismic and production data to generate a consistent reservoir description. It will introduce reservoir modeling workflow from construction of the 3D static reservoir model through up-scaling and dynamic reservoir simulation. The course provides background and insights to geo-statistical modeling techniques and the situations where the application of geo-statistics could add value. It will also provide guidance in the assembly and analysis of the required data for geo-statistical techniques and the resulting numerical models. The course includes extensive hands-on training and problem solving using public domain software.

Who Should Attend?

This course is designed for professional reservoir engineers, petrophysicists, geophysicists, geologists and asset managers

What You Will Learn:

  • Review of steps in building static reservoir model
  • Decision making under uncertainty
  • Variogram definition, calculations and physical meaning
  • Simple and ordinary kriging
  • Conditional simulations/sequential approaches
  • Indicator simulation of lithofacies
  • Point & block estimation
  • Integration of seismic data
  • Up-gridding and Up-scaling
  • Experimental design and applications
  • Flow simulation through geologic models using streamlines
  • History matching- preliminaries

Course Outline:

  • Introduction to petroleum geo-statistics in reservoir characterization and modeling
  • Review of probability and distributions
  • Covariance and correlation, analysis of spatial continuity, variogram definition, calculations and physical meaning
  • Modeling & interpreting the variogram
  • Cokriging/Collocated Cokriging
  • Boolean/Object-based models
  • Multidisciplinary data integration
  • Field case studies and hands-on practice

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