Fourth Session of Summer Internship Program Notes

Jul 29, 2024

Fourth Session of Summer Internship Program

Moderator: Mayor Tara, Senior Gas and Petrochemicals Engineering Student, SP Egypt Young Professionals Member.

Welcome

  • Welcome note by Mayor Tara
  • Chat box guidelines: Keep it professional and ethical
  • Q&A section for questions

Speaker: Dr. Ramy Alou

  • Technical Product Owner of Upstream Data at SNP Global Community Insight, USA
  • Specializes in Reservoir Engineering, Digitalization, Data Science
  • PhD from University of Wyoming, focusing on fluid phase behavior at nanoscale and AI in oil and gas industry
  • Master’s from King Fahd University of Petroleum and Minerals, Saudi Arabia
  • Consultant at Saudi Aramco's Reservoir Characterization Division
  • Bachelor's degree in Petroleum Engineering from SUS University, Egypt
  • Certified Machine Learning Engineer and Data Analyst from Amazon, Microsoft, Google

Introduction to Machine Learning in Oil & Gas

  • Lecture format: Hands-on and practical
  • Will use a public challenge as the course basis: less slides, more coding
  • Emphasis on learning through doing
  • Machine learning cannot be only taught via lectures; hands-on experience is crucial

Course Outline

  • General overview of Machine Learning (ML)
    • Definitions, differences between ML and AI
  • Application in oil and gas industry
    • Practical approach: doing and creating
    • Exploring a public challenge, less theoretical
  • Flexibility based on student feedback

Key Concepts

  • Artificial Intelligence (AI): Broad field, having computers perform tasks
  • Machine Learning (ML): Specific algorithms that improve performance with data training
  • Deep Learning: Subset of ML, multi-layered, vast amounts of data
  • Generative AI: Produces content based on learned data

Historical Development

  • 1950s: AI concept initiated with Turing Test; many algorithms developed
  • 2000s: Internet boom, massive data availability
  • 2012-Present: Breakthroughs due to compute power and big data

Importance of Big Data and Compute Power

  • Essential for ML success: analyzing vast amounts of data and powerful computing
  • Example: OpenAI's ChatGPT using 45PB of data (15 trillion tokens)

Application in Oil and Gas Industry

  • Predictive Maintenance: Sensor data for equipment maintenance
  • Reservoir Modeling: Data-driven versus physics-based models
  • Seismic Data Analysis: Identifying exploration targets, improving accuracy
  • Process Optimization: Enhancing efficiency
  • Predictive Analytics: Forecast trends and outcomes
  • Supply Chain Optimization
  • HSE: Health, safety, environmental monitoring and improvement

Practical Approach

  • Hands-on approach encouraged
  • Utilizing public platforms like Kaggle and Upstream Data Challenges
    • Kaggle: Comprehensive data sets, code notebooks, community support
    • Upstream: Challenges specific to industry issues (e.g., seismic analysis)

Typical Steps in ML Challenge

  1. Problem Definition: Clear objective, what to predict or classify
  2. Data Collection: Gathering and cleaning from varied sources
  3. Feature Engineering: Creating better representations for models
  4. Exploratory Data Analysis (EDA): Understanding data distributions
  5. Model Selection: Based on problem definition (regression, classification, etc.)
  6. Training and Evaluation: Split data, train model, test with unseen data
  7. Model Tuning: Hyperparameter tuning, ensure model generalizes well
  8. Deployment: Server deployment for widespread use

Conclusion

  • Break the barriers: start with available tools and data
  • Machine learning no longer depends on manual coding proficiency
  • Tools like ChatGPT make entry easier
  • Next sessions to include hands-on work with challenges
    • E.g., Spot-the-Trend: Pattern recognition in well pressure data

Q&A Highlights

  • Importance of coding: Useful but not a barrier due to modern tools
  • Relevance of SQL in oil & gas data processing
  • Multiple use cases discussed: seismic analysis, predictive maintenance, stress analysis, etc.

Next Steps

  • Course to continue with practical, hands-on activities
  • Participant feedback will shape the direction
    • E.g., Participation in challenges, use of basic starting projects on Kaggle

Additional Resources

Thank you to Dr. Ramy Alou for the informative session, and looking forward to future learning!