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    • Class and Course
    1. NExT
    2. Digital Technology
    3. Computer Vision for the Energy Industry

    Computer Vision for the Energy Industry

    • Advanced
    • 5 Days
    • classroom1Classroom

    The "Computer Vision for the Energy Industry" training is a five-day hands-on program that takes participants from the basics of neural networks all the way to the latest transformer architectures. Along the way, you'll build real models—starting with image classifiers, then moving into object detection and segmentation techniques that are directly applicable to tasks like seismic interpretation. The course also covers how to generate synthetic training data, a practical solution when real-world examples of defects or anomalies are scarce. By the end of the week, participants will have the skills to start applying computer vision to actual challenges in the energy industry.
    Technical professionals in the energy industry—including data scientists, engineers, and geoscientists—who have programming experience and want to build practical computer vision skills for industry applications.

    Programming

    Participants should have solid Python proficiency, including experience with functions, classes, and list comprehensions. Working knowledge of NumPy and Pandas for data manipulation is expected, as is familiarity with Jupyter notebooks or equivalent development environments. Participants should be comfortable managing packages using pip or conda.

    Mathematics

    A foundational understanding of linear algebra is essential, including vectors, matrices, matrix multiplication, and transpose operations. Basic calculus—particularly derivatives and the chain rule—is necessary to understand backpropagation during neural network training. Familiarity with core probability and statistics concepts such as distributions, mean, and variance will support comprehension of model evaluation and loss functions.

    Machine Learning Foundations

    Participants should understand the distinction between supervised and unsupervised learning, as well as the purpose of train/validation/test data splits. Concepts such as overfitting, underfitting, and standard evaluation metrics (accuracy, precision, recall) should be familiar. Prior experience training a basic model—such as logistic regression or a decision tree—is recommended but not required.


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    Petrotechnical

    Operations and Maintenance

    Energy Transition

    SLB Software

    Digital Technology

    Technical Challenges

    Health Safety and Environment (HSE)

    Business Management

    E&P Introduction and Fundamentals

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