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Oil & Gas Training
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    Oil and Gas Training Courses 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

    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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