Introduction to Python and Petroleum Data Analytics

Overzicht

ABOUT THE COURSE:

Petroleum Data Analytics (PDA) is rapidly transforming the oil and gas industry through the integration of Artificial Intelligence (AI) and Machine Learning (ML). As we look ahead, it's evident that mastering these technologies will be pivotal for shaping the future of engineering disciplines, particularly in petroleum engineering.This 12- weekcourse aims to equip the next generation of petroleum professionals with essential foundations in PDA.

While it's acknowledged that a single course cannot cover all aspects of becoming a PDA expert, it serves as a crucial starting point. Participants will gain a realistic understanding of AI and ML fundamentals as they apply to solving engineering challenges in the petroleum sector.For engineering-domain experts, transitioning into skilled AI and ML practitioners is becoming increasingly important.

The ability to harness data-driven insights through these technologies will not only optimize existing processes but also drive innovation in exploration, production, and operational efficiency within the industry.Ultimately, this course serves as a catalyst for enthusiasts and professionals alike to grasp the transformative potential of PDA. It's a step towards unlocking the future where data-driven strategies and advanced analytics play a central role in shaping the trajectory of the oil and gas industry.INTENDED AUDIENCE:

Undergraduate, post graduate and PhD students’ professional practitioner in the discipline of Petroleum Engineering, Petroleum Refinery Engineering, Chemical EngineeringPREREQUISITES:

Bachelor’s degree in any Engineering disciplineINDUSTRY SUPPORT:

ONGC,OIL, ESSAR, IOCL, CAIRN, GAIL

Lesprogramma

• 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

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Gegeven door

Prof. Archana

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Vakgebieden

Programming