💻 Essential Data Science for Petroleum Geoscientists and Engineers

Overview

📘 Course Description

This course is designed to bridge the gap between traditional petroleum subsurface disciplines and modern data science techniques. Participants will learn to apply statistical analysis, machine learning, and predictive modeling to geological, geophysical, and engineering data. Using Python and industry-standard tools, attendees will gain hands-on experience in transforming raw data into actionable insights for better reservoir characterization, production optimization, and risk reduction.

🎯 Learning Objectives

✅ Understand the fundamentals of data science and machine learning
✅ Apply key data wrangling and preprocessing techniques to subsurface data
✅ Build regression, classification, and clustering models for geoscience and engineering problems
✅ Visualize large datasets effectively
✅ Interpret machine learning results and integrate them into petroleum workflows
✅ Automate repetitive tasks and improve efficiency in reservoir analysis

👥 Who Should Attend

Geoscientists and geophysicists
Reservoir and production engineers
Petrophysicists
Data managers in E&P companies
Technical professionals seeking to upskill in data science

🗂️ Training Format

✅ Lectures with real-world subsurface data examples
✅ Interactive coding sessions in Python (Jupyter Notebooks)
✅ Hands-on exercises in data visualization, ML modeling, and interpretation
✅ Group discussions of applications to participants’ workflows
✅ Industry case studies highlighting data-driven decision making

📅 Detailed Daily Agenda with Time Breaks

📅 Day 1: Introduction to Data Science for Energy

Time	Topic
08:30 – 09:00	Welcome, Course Objectives & Introductions
09:00 – 10:30	Overview of Data Science in Oil & Gas
10:30 – 10:45	☕ Coffee Break
10:45 – 12:15	Basic Python for Data Analysis (NumPy, Pandas)
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Data Types & Structures for Geoscience
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Practical: Importing & Cleaning Subsurface Data

📅 Day 2: Exploratory Data Analysis & Visualization

Time	Topic
08:30 – 10:00	Descriptive Statistics & Data Summarization
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Data Visualization with Matplotlib & Seaborn
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Handling Missing & Outlier Data
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Practical: EDA on Petrophysical Logs & Seismic Attributes

📅 Day 3: Machine Learning Fundamentals

Time	Topic
08:30 – 10:00	Introduction to Supervised & Unsupervised Learning
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Regression Models for Reservoir Properties
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Classification Models (e.g., Lithofacies Prediction)
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Practical: Building and Evaluating ML Models

📅 Day 4: Advanced Modeling & Applications

Time	Topic
08:30 – 10:00	Clustering & Dimensionality Reduction (PCA, K-means)
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Feature Engineering with Subsurface Data
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Cross-validation & Avoiding Overfitting
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Practical: Predicting Reservoir Properties with ML

📅 Day 5: Integrated Workflows & Deployment

Time	Topic
08:30 – 10:00	Automating Workflows with Python Scripts
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Interpreting ML Results for Field Development
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Real-World Case Studies in Subsurface Data Science
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Group Presentations, Review, Q&A, & Certificate Ceremony

📦 Materials Provided

✅ Comprehensive digital course manual
✅ Python Jupyter Notebooks with exercises
✅ Example datasets (logs, seismic, production data)
✅ Certificate of Completion

Target audiences

Reservoir Engineers, Geologists

$ 5,950

Course Features

Lecture 0
Quiz 0
Duration 5 days
Skill level All levels
Language English
Students 40
Assessments Yes