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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.

    Segment 1 - length 3 or 4 hours depending on delivery format

    • Course and Instructor Introduction
    • Pre-course technical assessment
    • Participant Introductions

    • Review of digitization concept in the energy industry
    • Overview closed-loop reservoir surveillance and management
    • Outline the needs for employing machine learning and AI solutions that are inspired by physics for subsurface modeling and reservoir monitoring.
    • Learn the workflows for static and dynamic data inconsistency check with the physics.
    • Cover Intelligent Data Maid Workflows

    Segment 2 - length 3 or 4 hours depending on delivery format

    • Review Day 1 Key Concepts
    • Data reformatting
    • Data cleaning
    • Static data processing
    • Time series signal processing
    • Anomaly detection algorithms
    • Data categorization
    • Data segmentation and partitioning
    • Spatio-temporal database generation
    • Selected workshops
    • Case Studies

    Segment 3 - length 3 or 4 hours depending on delivery format

    • Review Day 2 Key Concepts
    • Teach all required concepts related to statistical learning, data mining and cover Intelligent Data Miner Workflows:
    • Statistical analysis
    • Generalize linear models development


    • Segment 4 - Duration 3 to 4 hours depending on delivery requirement
    • Review key concepts from Day 3 
    • Advanced dimensionality reduction techniques and algorithms
    • Clustering algorithms
    • Advanced KPI identification 
    • Case Studies
    • Selected Applied Workshops

    • Segment 5 - Duration 3 to 4 hours depending on delivery requirement
    • Review key concepts from Day 4 
    • Learn Intelligent Data Model workflows to build, test and validate the machine learning based models  
    • using different shallow and deep learning algorithms as well as hybrid learning concepts.
    • Selected workshops and practical sessions



    • Segment 6 - Duration 3 to 4 hours depending on delivery requirement
    • Review key concepts from Day 5  
    • Learn to interpret the developed intelligent models 
    • Gain insight into advanced Intelligent Interpretable Data Models construction
    • Complete selected workshops and practical sessions


    • Segment 7- Duration 3 to 4 hours depending on delivery requirement
    • Review key concepts from Day 6
    • Review general concepts of uncertainty
    • Uncertainty analysis and optimization for accurate techno-economic analysis  
    • Complete selected workshops and practical sessions

    • Segment 8 - Duration 3 to 4 hours depending on delivery requirement
    • Review key concepts from Day 7
    • Review selected case studies  - Part 1
    • Complete additional "hands on" workshops and practical sessions

  • Segment 9 - Duration 3 to 4 hours depending on delivery requirement
  • Review key concepts from Day 8
  • Part 2 - Review selected case studies 
  • Part 2 - Complete additional "hands on" workshops and practical sessions


  • Segment 10 - Duration 3 to 4 hours depending on delivery requirement
  • Review key concepts from Day 9
  • Review additional special topics
  • Review overall process and important validation steps
  • Complete post course technical assessment
  • Course wrap-up


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