🤖 Practical AI Fundamentals for Energy Professionals

Overview

📘 Course Description

This course demystifies Artificial Intelligence (AI) for professionals working in the energy sector, bridging the gap between AI technology and practical oil, gas, and renewable energy applications. Participants will gain a solid foundation in machine learning, deep learning, and data analytics concepts, learn to identify problems that can be solved with AI, and practice applying AI workflows to real-world energy challenges such as seismic interpretation, predictive maintenance, production forecasting, and reservoir analysis.

🎯 Learning Objectives

✅ Understand key AI concepts relevant to energy applications
✅ Identify opportunities to use AI in exploration, drilling, production, and energy operations
✅ Learn basics of machine learning (ML) and deep learning (DL) techniques
✅ Build simple predictive models using structured and unstructured data
✅ Apply AI workflows to solve practical problems in energy projects
✅ Evaluate AI models for accuracy, bias, and reliability

👥 Who Should Attend

Geoscientists, reservoir & petroleum engineers
Data analysts and data managers in energy companies
Project managers and technical team leads
Energy professionals looking to integrate AI into workflows
Technical decision-makers exploring digital transformation strategies

🗂️ Training Format

✅ Conceptual lectures explaining AI & ML fundamentals
✅ Hands-on labs using tools like Python, Jupyter Notebooks & popular ML libraries
✅ Energy-focused exercises and case studies
✅ Group discussions on AI strategy in energy companies
✅ Daily Q&A sessions

📅 Day-by-Day Agenda with Time Breaks

📅 Day 1: Introduction to AI in Energy

Time	Topic
08:30 – 09:00	Welcome, Introductions & Course Objectives
09:00 – 10:30	Overview of AI, ML, and DL: What They Are & Why They Matter
10:30 – 10:45	☕ Coffee Break
10:45 – 12:15	Key AI Concepts: Data, Algorithms & Features
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	The Role of AI in the Energy Industry: Trends & Case Studies
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Workshop: Identifying AI Opportunities in Your Projects

📅 Day 2: Fundamentals of Machine Learning

Time	Topic
08:30 – 10:00	Types of ML: Supervised, Unsupervised, Reinforcement Learning
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Data Preparation & Feature Engineering
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Building a Simple Regression Model in Python
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Workshop: Predicting Production Rates from Historical Data

📅 Day 3: Deep Learning & Applications in Energy

Time	Topic
08:30 – 10:00	Introduction to Neural Networks & Deep Learning
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Convolutional Neural Networks for Seismic Interpretation
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Time Series Modeling for Predictive Maintenance
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Workshop: Building a Fault Detection Model with Sensor Data

📅 Day 4: AI Workflows & Deployment

Time	Topic
08:30 – 10:00	AI Project Lifecycle: From Data to Model to Deployment
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Evaluating Models: Metrics, Overfitting & Bias
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Deploying AI Models: Tools & Infrastructure Basics
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Workshop: Deploying an AI Model for Production Forecasting

📅 Day 5: Integrating AI into Energy Workflows

Time	Topic
08:30 – 10:00	Building Data-Driven Cultures in Energy Companies
10:00 – 10:15	☕ Coffee Break
10:15 – 12:15	Ethics, Bias, and Trust in AI Systems
12:15 – 13:15	🍽 Lunch
13:15 – 14:45	Planning Your AI Strategy: People, Processes, Tools
14:45 – 15:00	☕ Coffee Break
15:00 – 16:30	Final Review, Case Studies, Q&A, Wrap-Up & Certificate Distribution

📦 Materials Provided

✅ Course manual & slides
✅ Jupyter Notebook templates & datasets for hands-on labs
✅ Cheat sheets for key Python & ML libraries
✅ Energy industry AI case study library
✅ Certificate of Completion

Target audiences

Reservoir Engineers, Geologists

$ 5,950