Objectives:
The goal is to equip participants with the knowledge and tools to apply data science techniques—such as clustering, dimensionality reduction, and supervised learning—to petrophysical datasets. The course focuses on transforming data into actionable insights for reservoir and production optimization.
Audience:
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.
Metodology:
Delivered in a live online format, the course combines instructor-led lectures with hands-on exercises. Participants will work with real-world datasets using Python and data visualization tools, exploring practical applications through guided workflows and collaborative group tasks.
Scope:
This course provides a practical introduction to applying data analytics and machine learning techniques in petrophysical workflows. It emphasizes how data-driven approaches can enhance the evaluation and integration of petrophysical data, reduce uncertainties, and support more robust reservoir characterization.
Content:
- 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.
