Objectives:
The course aims to introduce participants to modern data analytics techniques applied to petrophysical data. It focuses on understanding, integrating, and analyzing large datasets to improve formation evaluation, reduce uncertainty, and make better reservoir decisions using statistical and machine learning tools.
Audience:
This course is designed for petrophysicists, geoscientists, reservoir engineers, and data scientists working with well log data. It is also suitable for professionals seeking to bridge petrophysical interpretation with modern data analytics approaches.
Methodology:
The course uses a combination of lectures, hands-on exercises, and case studies. Participants will explore Python-based tools, workflows for log interpretation, and machine learning applications, gaining practical experience in data processing and predictive modeling.
Scope:
Covers data analytics fundamentals, feature engineering, supervised and unsupervised learning, clustering, regression, and classification. The focus is on practical applications in formation evaluation and integration of core, log, and production data to support reservoir characterization.
Course Program:
- 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.
