Oil & Gas Training
and Competency Development
Competency Management system SLB NEXT

Geostatistical Reservoir Characterization with Petrel

Workshop with both domain and software tasks enhancing the knowledge of Geostatistical Reservoir Characterization, from the static and dynamic perspectives.

“Geostatistical Reservoir Characterization with Petrel” is a one-week, classroom-based blended domain and software training. The objective of this training was to focus specifically on the characteristics, challenges, technologies and industry best-practices applicable to geostatistical analysis, and reservoir characterization from a geostatistical perspective, applying different modeling techniques, conditioning simulated models and analyzing geo-uncertainties as part of the static model definition and de-risking using a synthetic dataset.

The course provides the foundations of geostatistical descriptive and spatial analysis by covering the theoretical-applied aspects in both deterministic and stochastic modeling approaches.
It offers a wide perspective of best-practices to generate facies, litho-class, porosity, permeability, NtG and water saturation modeling techniques, and opens for assessing the uncertainties associated to those models.

The tasks and challenges are done in a team effort, where the instructor lead the sessions and students through discussion and active participation develop their own answers. That opens for discussions on different possible results.

This course can be a modular piece to longer training programs.

Day 1
  •  Introduction, brief recap of Heavy Oil for SEPLAT class, and SEPLAT project structuring
  •  Introduction to Modeling
  •  Univariate data analysis
  •  Upscale well log data and support change
  •  Declustering techniques
  •  The influence of the structure and layering
  •  Bivariate analysis
Day 2
  • Spatial analysis and variance
  • Interpreting discrete logs using manual interpretations, Calculator statements and neural Networks.
  • Data Analysis for discrete logs: Proportions, Probability and Variance
  • Secondary data variance analysis
  • Geometrical trend modeling
  • Discrete and continuous trend modeling
Day 3
  • Stochastic simulation
  • Discrete modeling techniques overview
  • Discrete pixel-based techniques: Sequential Facies Simulation
  • Discrete object-based techniques
  • Conceptual modeling using training images with multipoint statistics
  • Hierarchical facies modeling
Day 4
  • Data analysis with continuous properties: Transformations and the journey towards Normally Score Transformed distributions and Variance analysis.
  • Deterministic techniques for continuous properties: Kriging and calculator statements.
  • Continuous conditioned simulation techniques: Gaussian simulation.
  • Conditioning petrophysical models: Best practices for Collocated Co-Kriging and Bivariate distributions.
  • Challenge task: Create porosity, permeability, Net to Gros and Water Saturation models.
  • Utilization of dynamic data in geological modeling update
Day 5
  • Case Study: Utilization of dynamic data in geological modeling update
  • Introduction to Uncertainty Analysis
  • Probabilistic and stochastic uncertainty
  • Uncertainty and Sensitivity Analysis with focus on the structural and property modeling parameters.
  • Scenario based uncertainty analysis
  • o Case study: Ranking realizations based on dynamic behavior of the reservoir using Geoscreening
Learning activity mix

Geoscientists, Geomodelers, Reservoir Engineers.




Modeling Techniques

Unertainty Analysis

Basic Petrel knowledge

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