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    • Class and Course
    1. NExT
    2. Petrotechnical
    3. RILS - Applied Machine Learning for Reservoir Characterization, Petrophysics and Surveillance - Physics Inspired Principles, Applications and Workflows

    RILS - Applied Machine Learning for Reservoir Characterization, Petrophysics and Surveillance - Physics Inspired Principles, Applications and Workflows

    • Skill
    • 10 Days
    • Remote Instructor Led SeriesRemote Instructor Led Series
      • Average client rating (based on 11 attendee reviews)

    This is the remote delivery version of the course "Application of AI and Machine Learning (ML) for Reservoir Characterization, Petrophysics and Surveillance - Physics Inspired Principles, Applications and Workflows". Machine learning is become a new addition to the traditional reservoir characterization, petrophysics and monitoring practice. This version is delivered in 10 segments that are either 3 or 4 hours in duration. This course can be customized to fit the client needs. 

    Developed physics based data models are the key for applying ML techniques to solve complex problems. In other words, the machine should learn the right physical concepts using the available data to produce correct insights that we are looking for.

    In addition quality data collection, physics-inspired predictive capability, and interpretability of the data-driven models should be considered.

    Participants learn the elements of building machine learning-based models form initial data processing to predictive model development. Development of online and automated learning models will be discussed and demonstrated. Several case studies will be presented in the class and participants will practice developing a data-driven model during the class.

    Development of the right ML-based model is started with applying intelligent data workflows to clean, quality control against the physics and generate the spatiotemporal database for the training, validation, prediction and optimization processes.

    The quality of static data will be assessed. Any inconsistency and anomaly in dynamic data (pressure, rate etc) should be addressed before employing any data mining and machine learning algorithms.

    Currently there are no scheduled classes for this course.

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