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  • Class and Course

    Hands on Machine Learning for Geoscientists and Engineers

    The five-day in-person course will focus on imparting practical ML skills for geological and reservoir engineering applications. Utilizing Python as the programming language, participants will be exposed to a comprehensive curriculum that covers exploratory data analysis, data preparation for modelling, and the application of fundamental ML concepts to real-world industry scenarios.

     

    In our daily class, we follow a three-part structure. We begin each session with a review of fundamental concepts, ensuring a strong foundation of knowledge. Next, we dive into live coding demos where you actively participate, experiencing hands-on learning as we code together. Finally, we culminate each session with guided exercises that provide valuable practical experience, focusing on real-world geological and engineering tasks and datasets. Get ready for an immersive learning journey that combines theory, application, and exploration in the field of ML for geoscientists and engineers.

    Hands-on Activities

    • Volumetric calculations with NumPy 
    • Formation Evaluation and EDA with Pandas 
    • Type well analysis with Pandas 
    • Well log prediction 
    • Predicting cumulative production
    • Facies analysis and clustering 
    • Production trends prediction using clustering analysis
    • 3D Facies modelling using ML
    • CO2 emissions prediction (Time series)




    Introduction

    • What is Machine Learning (ML)?
    • Examples of ML applications in geosciences and reservoir engineering

     

    Python crash course

    • Python libraries commonly used in ML
    • Variables, data types and basic operations
    • List, dictionaries, and loops
    • Functions

     

    NumPy

    • Filtering
    • Indexing
    • Slicing

    Exploratory Data Analysis (EDA)

    • Importance of data preprocessing and cleaning
    • Feature engineering: extracting meaningful features from data

     

    Pandas

    • Data cleaning
    • Data manipulation
    • Data visualization

    ML Overview

    • Types of ML tasks
    • ML workflow: data splitting, model training, validation, and evaluation
    • Model validation techniques: cross-validation
    • Understanding model errors and the bias-variance trade-off
    • Learning curves: assessing model performance

     

    ML for Regression

    • Fundamental algorithms
    • Cross Validation
    • Feature importance: SHAP values

    ML for Clustering

    • Handling high-dimensional data: the curse of dimensionality
    • Dimensionality reduction techniques: principal component analysis (PCA), UMAP
    • Interpreting and evaluating clustering results


    ML for Classification

    • Common metrics for classification: accuracy, precision, recall, F1-score
    • Confusion matrix
    • Grid Search and randomized grid search for hyperparameter optimization
    • Model evaluation and comparison


    The course is designed to accommodate those with no prior coding experience by introducing Python basics and guiding participants through hands-on exercises using geological and reservoir engineering datasets.


    UPCOMING CLASS
    Calgary, Alberta, Canada
    August 26-30, 2024
    $3,000

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