Introduction to Python and Petroleum Data Analytics

Overview

Master Python programming and petroleum data analytics, exploring AI and ML applications in the oil and gas industry through data manipulation, algorithms, and regression techniques for engineering challenges.

Syllabus

- Introduction to Python and Petroleum Data Analytics - Course Overview -- Introduction to the course objectives and outcomes -- Importance of AI and ML in petroleum engineering - Week 1-2: Python Programming Essentials -- Python syntax and basic programming concepts -- Data structures: lists, tuples, dictionaries -- Functions and modules in Python - Week 3-4: Data Handling with Python -- Introduction to NumPy and Pandas -- Data cleaning and preprocessing -- Data visualization techniques using Matplotlib and Seaborn - Week 5-6: Introduction to Machine Learning -- Basics of machine learning concepts -- Supervised vs. unsupervised learning -- Introduction to scikit-learn - Week 7-8: Machine Learning Applications in Petroleum Engineering -- Case studies of ML in exploration and production -- Predictive modeling for reservoir management -- Pattern recognition in seismic data - Week 9-10: Advanced Analytics in Petroleum Data -- Time series analysis for production data -- Anomaly detection in drilling operations -- Optimization techniques in reservoir management - Week 11: AI-driven Innovation in the Oil and Gas Industry -- Impact of AI on operational efficiency -- Innovative uses of ML in petroleum exploration - Week 12: Project Work and Future Directions -- Capstone project: Real-world PDA application -- Discussion on emerging trends and technologies - Course Wrap-up -- Review of key concepts -- Q&A and feedback session

Taught by

Prof. Archana

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