Course Schedule

Description

The Petroleum Data Analytics course is designed to bridge the gap between traditional petroleum engineering and modern data analytics. Participants will learn how to utilize data-driven insights to optimize petroleum exploration, production, and reservoir management. The course covers essential concepts, tools, and techniques in data analysis, tailored specifically for the petroleum industry, equipping professionals with the ability to make informed decisions and solve complex challenges.

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Course Description

Introduction


In the rapidly evolving oil and gas industry, data analytics has become a vital tool for optimizing operations and enhancing decision-making processes. This Petroleum Data Analytics course is crafted to provide participants with the knowledge and skills to turn raw data into valuable insights. With a focus on practical applications, the course aims to transform the way professionals approach data in petroleum engineering.

Objectives


  • Understand the fundamentals of data analytics in the context of petroleum engineering.
  • Learn to apply data analytics techniques to solve industry-specific challenges.
  • Gain proficiency in using data tools and software relevant to the oil and gas sector.
  • Develop skills to optimize petroleum production and reservoir management using data insights.
  • Enhance decision-making processes through predictive analytics and data-driven strategies.
  • Training Methodology


    This course employs a blend of theoretical knowledge and practical exercises. Participants will engage in hands-on activities using real-world data sets, case studies, and interactive discussions. The methodology ensures a comprehensive understanding of data analytics principles, with direct application to the petroleum industry.

    Organisational Impact


  • Improved decision-making processes based on data-driven insights.
  • Enhanced productivity and operational efficiency in petroleum exploration and production.
  • Ability to leverage data analytics for competitive advantage in the market.
  • Strengthened problem-solving capabilities within teams.
  • Increased return on investment through optimized petroleum operations.
  • Personal Impact


  • Gain in-depth knowledge of data analytics principles applied to petroleum engineering.
  • Enhance your skills in using data tools and techniques for solving complex problems.
  • Boost your career prospects with advanced data analytics capabilities.
  • Develop a strategic mindset for making informed decisions based on data.
  • Stay ahead in the industry by adopting modern data-driven practices.
  • Who Should Attend?


    This course is ideal for professionals in the oil and gas industry, including:


    • Petroleum engineers and geoscientists
    • Data analysts and data scientists working in the energy sector
    • Production and reservoir engineers
    • Technical managers and decision-makers in oil and gas companies
    • Anyone interested in leveraging data analytics to enhance petroleum operations

    Course Outline


    • Day 1 - Artificial Intelligence and Machine Learning: Theoretical Background
    • Day 2 - Petroleum Data Analytics
    • Day 3 - AI-based (Top-Down) Reservoir Simulation and Modeling
    • Day 4 - AI-based Proxy Modeling of Numerical Reservoir Simulation: Smart Proxy Modeling
    • Day 5 - AI-based Completion and Production Optimization of Shale Wells: Shale Analytics

    Certificates


    On successful completion of this training course, PEA Certificate will be awarded to the delegates

    About The Trainer
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    Professor Shahab D. Mohaghegh is a pioneer in AI, Machine Learning, and Data Mining for the oil and gas industry, and serves as a professor at West Virginia University and CEO of Intelligent Solutions, Inc.

    He holds advanced degrees in petroleum and natural gas engineering, authored three books, over 170 technical papers, and led 60+ industry projects.

    A recognized leader, he is an SPE Distinguished Lecturer, featured author in SPE’s Journal, and founder of the SPE Petroleum Data-Driven Analytics section.

    Honored for his contributions, he played a key role in post-Deepwater Horizon efforts and advised on U.S. and ISO standards for Carbon Capture and Unconventional Resources.