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    1. NExT
    2. Petrotechnical
    3. Machine Learning for Facies Prediction | Blended course

    Machine Learning for Facies Prediction | Blended course

    • Foundation
    • 16 Hours
    • Remote Instructor Led SeriesRemote Instructor Led Series

    Brief Course description

    The aim of the course is to introduce how Machine Learning (ML) is used in Facies along a well-bore. It will give an understanding of the “workflows” used in ML. The used algorithms can be studied separately using references. Power-point presentations and videos will introduce various aspects of ML, but the emphasis is on computer-based exercises using open-source software.

    Learning methods and tools

    At the end of the course participants will have a clear idea how Machine learning, being part of Artificial Intelligence will impact the future of Geosciences. This will be evident from the examples of Machine Learning discussed and applied to the case of predicting lithology and pore fluids.


    Part 1Part 1 Instructor onlyPart 2Part 2 Instructor onlyAgenda

    Part 1

    • Introduction
    • ML intro
    • Moodle / Weka
    • Classification
    • Supervised

    Part 2

    • Clustering
    • Artificial NN
    • Semi-supervised
    • Regression
    • Ensemble
    • Deep NN

    Part 3

    • Over & underfitting
    • Forward, backward
    • Activation functions
    • SVM

    Part 4

    • Future ML in geophysics
    • Boolean Logics
    • Assist Acq & Proc
    • KnowledgeFlow
    • Google Colab

    Part 5

    Project Clustering and Classification


    All those interested in understanding the impact Machine Learning will have on the Geosciences and then as an example the impact on lithology and pore-fluid prediction. Hence, geologists, geophysicists, and engineers, involved in exploration and development of hydrocarbon or mineral resources.


    The lectures and exercises deal with pre-conditioning the datasets (balancing the input classes, standardization & normalization of data) and applying several methods to classify the data: Bayes, Logistic, Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees. Non-linear Regression is used to predict porosity. Use will be made of an open-source package called Weka. The reason is that it is a user-friendly package with most relevant Machine learning algorithms, except truly Deep Learning. This suffices for most exploratory applications, where we like to learn the workflows and applications of Machine learning. Therefore, I have included an introduction to Google Colab. This runs on the Cloud and allows use of a GPU. It is “the way” to learn using a whole range of open-source Machine Learning algorithms. In an exercise you will get acquainted with using interactive python notebooks, how to get algorithms using Scikit-Learn (sklearn) and if you restrain yourself from using it in earnest on large datasets, it is free.


    A basic understanding of Geophysics and Statistics. A Pre-requirement quiz can be taken by participants to check whether their knowledge of Geophysics and Statistics is sufficient to follow the course.


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