×
1

    • Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey
    Customize Your Learning Journey

    Class and Course
    1. NExT
    2. Petrotechnical
    3. Applied Statistical Modeling and Data Analytics - RILS

    Applied Statistical Modeling and Data Analytics - RILS

    • Skill
    • 10 Days
    • Remote Instructor Led SeriesRemote Instructor Led Series
      • Average client rating (based on 21 attendee reviews)

    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

    Currently there are no scheduled classes for this course.

    Click below to be alerted when scheduled

    Set a training goal, and easily track your progress

    Customize your own learning journey and track your progress when you start using a defined learning path.

    Icon
    In just few simple steps, you can customize your own learning journey in the discipline of your interest based on your immediate, intermediate and transitional goals. Once done, you can save it in NExTpert, the digital learning ecosystem, and track your progress.
    Explore

    SOLUTIONS

    Corporate

    Individual

    COURSE OFFERINGS

    Petrotechnical

    Operations and Maintenance

    New Energy

    SLB Software

    Digital Technology

    Technical Challenges

    Health Safety and Environment (HSE)

    Business Management

    E&P Introduction and Fundamentals

    Gamification

    © 2024 SLB Limited. All rights reserved.