Face-to-Face Course
June 16 to 20 , 2025
Duration : 5 days
Level: Basic – Intermediate
WHO SHOULD ATTEND: Geologists, petrophysicists and log analysts who want to learn how to leverage data science tools and Python to prepare, process and interpret well log data.
COURSE CONTENT AND OBJECTIVES:
This course aims to elevate the data analytics skills of petrophysics professionals. Participants will master Python fundamentals, acquire advanced data manipulation techniques using Pandas, and learn event detection in petrophysical datasets. The course covers predictive modeling for estimating petrophysical properties, utilizing specific libraries like LASIO and WELLY, and applying Python libraries such as Matplotlib and Seaborn for visualization. It introduces artificial intelligence and machine learning techniques for predicting petrophysical properties using real field data. Participants will develop modeling analysis skills, integrate AI and ML into workflows, and apply acquired knowledge in real-world scenarios through practical exercises and case studies.
Course content includes:
- Introduction to course objectives and expectations.
- Overview of potential data sources for petrophysics.
- Introduction to sample datasets and real-life log analysis examples in petrophysics.
- General and Log Analysis specific Python libraries including NumPy, Pandas, Matplotlib, Seaborn, LASIO and WELLY
- Basic to Advanced Pandas operations: handling missing data, merging datasets, grouping, and aggregating data.
- Event detection in petrophysical data: understanding events and implementing rule-based event detection using Python.
- Statistical analysis in Python: t-tests and hypothesis testing.
- Introduction to predictive modeling in petrophysics and log analysis.
- Implementing regression models for predicting petrophysical properties and statistical regression analysis for petrophysical data.
