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The course reviews the full spectrum of an integrated reservoir study from core/well log to fluid flow simulator. It addresses the practical requirements and workflows for modern 3D reservoir modeling. Participants learn how to use deterministic and stochastic modeling methodologies to quantitatively integrate diverse data types, model reservoir heterogeneity, assess model uncertainty, and prepare the reservoir model as input to a flow simulator, Particular emphasis is placed on the best modeling practices and data integration methodologies available using geostatistical techniques. The presentation includes an informal, interactive discussion of the agenda topics, illustrated with case studies and including computer workshops.
The objective of this 5-day course is to provide participants who have experience in building reservoir models, with the solid understanding of the underlying geostatistical methodologies used in Applied Reservoir Modeling.
The course provides discussion of the heart of geostatistics that includes data stationarity, spatial relationship (variogram), estimation (kriging and cokriging), and conditional simulation (SIS, TGS, SGS). Additionally, best practices on the workflow for building reservoir model will be presented. To enhance the understanding of the participants about these topics, the exercise for detail analysis and sensitivity, based on actual field data, is provided. All exercises will be done using PETREL.
This course is important for reservoir modelers or for any geoscientist/engineer involved in an integrated reservoir study. It is of particular relevance to reservoir modelers that are interested in getting a better understanding on the “black-box” of geostatistics techniques commonly found during the facies and property modeling process. It will also improve skills in the art of data integration.
Suggested course textbook, "Applied Geostatistics for Reservoir Characterization" by Kelkar and Perez. Cost via SPE (member price) is US$60 plus shipping.
o From Core to Simulation : An Integrated Reservoir Modeling Workflow
o Role of Heterogeneity in Reservoir Modeling
o Fundamentals of Geostatistics
• Basic Statistics for Reservoir Modeling
o Descriptive Statistics : Univariate and Bi-variate Analysis (Linear Regression)
o Inferencial Statistics : Random Experiment, Probability and Probability Distribution Function (PDF)
• Practical Session 1
o Advantage and Disadvantage of Linear Regression
o Gaussian TransformDay 2
• Data Stationarity
o Background Theory : Outlier, Log ,Trend (1D, 2D and 3D) and Normal Transforms
o Practical Session 2 : Performing Data Analysis of Continuous and Discrete Variables
• Spatial Analysis :
o Covariance and Variogram in Spatial Space
o Variogram Analysis : Definition, Variogram in Practice, Modeling, Anisotropy, and Uncertainty
o Practical Session 3 : Building Variogram ModelDay 3
• Deterministics Modeling : Estimation Process (Kriging)
o Kriging vs. Conventional Linear Interpolation
o Kriging Fundamental : Simple and Ordinary Kriging
o Kriging for Geological Facies
o 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 TechniquesDay 4
• Stochastic Modeling : Stochastic Simulation
o Preserving Heterogeneity : Estimation vs. Simulation
o Continuous Variable Simulation using Sequential Gaussian Technique
o Discrete Variable Simulation using Sequential Indicator and Truncated Gausssian Technique
o Co-Simulation Process for Data Integration
o Practical Session 4 : Sensitivity of Simulation TechniquesDay 5
• Practical Integrated Reservoir Modeling Process
o Structure Modeling
o Facies Modeling (Geological Facies and Rock Type)
o Property Modeling (Porosity, Permeability, and Saturation)
o Volume Calculation and Uncertainty Analysis
o Moving to Dynamic Model
- Dynamic Ranking
Professional geologists, petrophysicists, geophysicists, engineers, managers & professional technicians
Introduction - Fundamentals of Geostatistics
Basic statistice for reservoir modeling
Data stationarity and trends
Spatial analysis - Variograms
Modeling using Kriging and Cokriging
Stochastic simulation and cosimulation
Creating the static model - framework and properties
Moving to the dynamic model - upscaling
Knowledge of Geological techniques and processes ; structure and sedimentation. Understanding of Static & Dynamic reservoir modeling.
Knowledge of PETREL software is an advantage. Suggested completion of Petrel fundamentals training.
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