By the end of this training course, participants will learn to:

• To understand geostatistical methods.
• To apply geostatistical models to evaluate the data.
• To understand the concepts of Kriging and cokriging and how these are used in data analysis.
• To develop a conceptual geological model ahead of a static model building.
• To develop a sound structural model using regional data applied down to the model scale.
• To develop and test a stratigraphic model.
• To be able to develop a facies model and how to test this against analogs.
• To be able to determine petrophysical and property models.
• To know how to generate an accurate 3D static model by integrating all the above.
• How to integrate with reservoir engineer to match simulation data.

The course will not only be presented by showing and interpreting the material in detail, but also the

participants will work together using real data to apply all the workflow and to project their previous

knowledge and experience onto the course, they are also encouraged to bring their own data so that real

working examples can be reviewed and interpreted.

This interactive Training will be highly interactive, with opportunities to advance your opinions and ideas and will include:

• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

Module 1: Data Conditioning and QC.

• Collecting facies and petrophysical data to be read for modeling.
• Comparing porosity and facies.
• Modeling Uncertainty o Geophysical Uncertainty.

o Geological Uncertainty.

o Structural Uncertainty.

o Petrophysical Uncertainty.

o Fracture Uncertainty

o Contact Uncertainty.

• Choosing suitable seismic inversion product to be used as weighting input for data distribution.

Module 2: Spatial Analysis and Modelling

• General Log Measurement Terminology
• Electric log correlation procedures and guidelines.
• Electrical log correlation in vertical wells
• Log correlation plan
• Basic concepts in electric log correlation
• Faults Vs variation in stratigraphy
• Electrical log correlation in – directional drilled wells
• Log correlation plan correlation of vertical and directional drilled wells
• MD, TVDss, TVD, TVT and TST terminologies.

Module 3: Structure Modeling.

• Pre-Processing of input data.
• Fault Modeling.
• Horizon Modeling.
• Layering.
• Structure Frame work.
• 3D Structural Grid Construction.
• Boundary definition and Horizon modeling.
• Horizon filtering attribute. • Refine and create a zone model.
• Troubleshooting.

Module 4: Horizon Modelling

• Corner Point Gridding.
• Modeling of main faults.
• Pillar gridding.
• Make horizons.
• Truncations.
• Data preparation, including well-log edits and calculations as well as log upscaling for discrete and

continuous data.

Module 5: Scaling up Well logs

• Scaling up facies’ logs.
• Averaging methods and their impact on upscaled facies logs.
• Scaling up petrol physical logs
• Averaging methods and their impact on upscaled petrol physical logs.

Module 6: Building the 3D Property Facies Model.

• Property Modeling Work Flow.
• Reservoir Modeling.
• Create a Facies Template.
• Net to Gross.
• Neural Net Work.
• Petrophysical Calculations.
• Exercises.

Module 7: Building the 3D Property Facies Model.

• Deterministic and stochastic facies modeling (object and pixel modeling).
• Developing a conceptual geological model.
• Data analysis.
• Facies probability function and its importance for facies distribution.
• Facies variogram analysis and how it affects its distribution through the model.
• Sequential Indicator Simulation.
• Object Facies Modeling.
• Truncated Gaussian Simulation with and without trends and use for carbonate reservoirs.
• Using secondary data to populate facies models.
• Developing a stratigraphic model
• The use of analogs in model builds
• How to build an accurate facies model and how to provide geological controls on this. Module 8: Building the 3D property Petrophysical Model.
• Deterministic and stochastic petrophysical modeling
• Data analysis.
• Sequential Gaussian Simulation.
• Gaussian Random Function Simulation.
• Kriging.
• Using secondary data to populate petrophysical models.
• Porosity and water saturation distribution through the model.
• How to weight water saturation distribution in the model.
• Permeability distribution in the model.

Module 9: Uncertainty Analysis, Ranking and Upscaling

• Building the final 3D model
• Uncertainty analysis and risk
• The space of uncertainty and pragmatic decisions
• First, second and third-order changes to the model
• Multiple realizations
• Developing risk maps
• Ranking and upscaling – passing the model on.

Module 10: Hydrocarbon in place calculations

• Monte Carlo Hydrocarbon Calculations based on structure contour maps.
• 3D static model hydrocarbon in place calculations.
• Validation of final in place with Monto Carlo assumptions.