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

Applied Statistical Modeling and Data Analytics - RILS

This Remote Instructor Led Course seeks to provide a practical guide to many of the classical and modern statistical techniques that have become, or are becoming, mainstream for oil and gas professionals. It is intended to serve as a “how to” guide for the practicing petroleum engineer or geoscientist interested in applying statistical modeling and data analytics techniques in reservoir characterization, reservoir modeling/diagnostics and performance predictions. Examples related to both conventional and unconventional reservoirs will be presented and the participants will analyze data using public domain software.

Day 1

Module 1 (4 hours)

  • Introduction - Overview and An Illustrative Example (45 minute)
  • Discussion (15 min)
  • Statistical Background and Exploratory Data Analysis (30 minute)
  • Computer Exercises: SGEMS (30 minutes)
  • Break (15 minutes)
  • Regression Modeling and Applications (45 minutes)
  • Discussion (15 minutes)
  • Computer Exercises (45 minutes)
  • Wrap up (15 minutes)
Day 2

Module 2 (4 hours)

  • Modeling Spatial Variation (45 minute)
  • Discussion (15 min)
  • Computer Exercises: SGEMS (45 minutes)
  • Break (15 minutes)
  • Spatial Modeling/interpolation of Properties (45 minute)
  • Discussion (15 min)
  • Computer Exercises: SGEMS (45 minutes)
  • Wrap up (15 minutes)
Day 3

Module 3 (4 hours)

  • Optimal Transformation for Multiple Regression(45 minute)
  • Computer Exercises: GRACE (30 minutes)
  • Non-Parametric Regression (30 minute)
  • Break (15 minutes)
  • Variable Selection: Stepwise Regression (45 minute)
  • Discussion (15 minutes)
  • Computer Exercises (45 minutes)
  • Wrap up (15 minutes)
Day 4

Module 4 (4 hours)

  • Principal Component Analysis (45 minute)
  • Discussion (15 min)
  • Computer Exercises: EFACIES (30 minutes)
  • Break (15 minutes)
  • Cluster Analysis (45 minute)
  • Discussion (15 min)
  • Computer Exercises: EFACIES (30 minutes)
  • Introduction to RATTLE (30 minute)
  • Wrap up (15 minutes)
Day 5

Module 5 (4 hours)

  • Classification and Regression Trees (45 minutes)
  • Discussion (15 min)
  • Computer Exercises: RATTLE (30 minute)
  • Advanced Machine Learning Methods: RF, GBM, SVM, NNET (45 minute)
  • Break (15 minutes)
  • Advanced Machine Learning Methods: RF, GBM, SVM, NNET (30 minute)
  • Computer Exercises: RATTLE (45 minutes)
  • Wrap up (15 minutes)
Day 6

Module 6 (4 hours)

  • Mid-Course Review and Recap of Key Concepts (60 minutes)
  • Discussion( 15 minutes)
  • Break (15 minute)
  • Application: Rate Decline in Unconventional Reservoirs (30 minute)
  • Computer Exercise: GRACE (30 minute)
  • Application: Hybrid Reservoir Modeling (30 minute)
  • Application: Surrogate Modeling (45 minute)
  • Wrap up (15 minutes)
Day 7

Module 7 (4 hours)

  • Experimental Design and Response Surface Analysis (45 minutes)
  • Discussion (15 minutes)
  • Experimental Design and Response Surface Analysis (45 minutes)
  • Break (15 minutes)
  • Computer Exercises: EREGRESS (30 minutes)
  • History Matching Using Response Surface and Genetic Algorithm (45 minutes)
  • Computer Exercises: GLOBAL (30 minute)
  • Wrap up (15 minute)
Day 8

Module 8 (4 hours)

  • Review of Key Concepts so far (45 minutes)
  • Discussion (15 minutes)
  • Break (15 minutes)
  • Data-Physics Modeling: Rate and Pressure Data Analysis (45 minutes)
  • Computer Exercises: SPADES (30 minutes)
  • Data-Physics Modeling: Applications (45 minutes)
  • Break (15 minutes)
  • Discussion and wrap up (30 minute)
Day 9

Module 9 (4 hours)

  • Review and recap (45 minutes)
  • Discussion (15 minutes)
  • Break (15 minutes)
  • Uncertainty Quantification: Background (45)
  • Discussion (15 minutes)
  • Uncertainty Characterization (30 minute)
  • Uncertainty Propagation (30 minute)
  • Uncertainty Importance Analysis (30 minute)
  • Wrap up (15 minute)
Day 10

Module 10 (4 hours)

  • Wrap-up and Key Takeaways (45 minutes)
  • Discussion (30 minutes)
  • Break (15 minutes)
  • Review and Discussion (60 minutes)
  • Workshop Conclusions
Learning activity mix

Geoscientists, Engineers

• Visualizing univariate, bivariate and multivariate data
• Fitting simple and multiple linear regression models to observed data
• Developing a non-parametric regression model from given data
• Reducing data dimensionality with Principal Component Analysis
• Grouping data with k-means and hierarchical clustering
• Identifying classification boundary between clusters using discriminant analysis
• Applying machine learning techniques (e.g., random forest, gradient boosting machine,
support vector regression, kriging model, neural networks) for predictive modeling
• Generating decision rules with classification tree analysis
• Translating model input uncertainty into uncertainty in model predictions using Monte Carlo
simulation and analytical alternatives
• Analyzing input-output dependencies from Monte-Carlo simulation results
• Creating an experimental design and fitting a response surface to the results
• Hybrid modeling combining data-driven and physics-based models

None

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