Online Live Streaming Course
October 14 to 18 , 2024
Duration : 20 hours
Level : Basic
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.
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.
BENEFITS:
This comprehensive course offers petrophysics professionals a transformative learning experience, providing mastery in Python programming, advanced data manipulation, and event detection techniques. Participants gain practical skills in predictive modeling for accurate estimation of petrophysical properties using real field data. The utilization of petrophysics-specific libraries such as LASIO and WELLY, coupled with proficiency in Python libraries like Matplotlib and Seaborn, enhances their ability to efficiently handle, visualize, and analyze diverse datasets. The introduction to artificial intelligence and machine learning empowers professionals to integrate cutting-edge techniques into their workflows, ultimately enabling them to make informed predictions. With a focus on modeling analysis and real-world application, this course equips participants with the expertise to address complex challenges in petrophysics.
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.
