Module 1

• Introduction

From Core to Simulation : An Integrated Reservoir Modeling Workflow
Role of Heterogeneity in Reservoir Modeling
Fundamentals of Geostatistics
• Basic Statistics for Reservoir Modeling

Descriptive Statistics : Univariate and Bi-variate Analysis (Linear Regression)
Inferencial Statistics : Random Experiment, Probability and Probability Distribution Function (PDF)
• Practical Session 1

Advantage and Disadvantage of Linear Regression
Gaussian Transform
Module 2
• Data Stationarity

Background Theory : Outlier, Log ,Trend (1D, 2D and 3D) and Normal Transforms
Practical Session 2 : Performing Data Analysis of Continuous and Discrete Variables
• Spatial Analysis :
Covariance and Variogram in Spatial Space
Variogram Analysis : Definition, Variogram in Practice, Modeling, Anisotropy, and Uncertainty
Practical Session 3 : Building Variogram Model
Module 3
• Deterministics Modeling : Estimation Process (Kriging)

Kriging vs. Conventional Linear Interpolation
Kriging Fundamental : Simple and Ordinary Kriging
Kriging for Geological Facies
Role of Secondary Variable (Seismic and/or Concept) :
– Preparing Secondary Variable
– Reconciling Data Resolution
– Universal Kriging (Trend Method)
– Co-Kriging and Collocated Cokriging
– Practical Session 4 : Sensitivity of Various Kriging Techniques
Module 4

• Stochastic Modeling : Stochastic Simulation

Preserving Heterogeneity : Estimation vs. Simulation
Continuous Variable Simulation using Sequential Gaussian Technique
Discrete Variable Simulation using Sequential Indicator and Truncated Gausssian Technique
Co-Simulation Process for Data Integration
Practical Session 4 : Sensitivity of Simulation Techniques
Module 5

• Practical Integrated Reservoir Modeling Process

Structure Modeling
Facies Modeling (Geological Facies and Rock Type)
Property Modeling (Porosity, Permeability, and Saturation)
Volume Calculation and Uncertainty Analysis
Moving to Dynamic Model
– Dynamic Ranking
– Upscaling