Artificial Intelligence in the Oil and Gas Industry

Course ID : AI-005

Duration In-class (в days) : 16 academic hours

Duration Online : 16 academic hours

Сurriculum : in-class, Virtual Instructor-Led Training - ONLINE

Delivery : 07.09.2026 - 09.09.2026

Overview

The course “Artificial Intelligence in the Oil and Gas Industry” is designed for executives and specialists responsible for the digital transformation of oil and gas companies.

The course program covers key aspects of implementing AI solutions to improve process efficiency.

Particular attention is paid to the application of machine learning, computer vision, and predictive analytics technologies at oil and gas facilities.

The practical portion of the course examines real-world AI implementation cases: predictive equipment maintenance systems, automation of instrumentation and automation systems (I&C), emissions and environmental monitoring, product quality management, as well as improving energy efficiency and optimizing technological processes.

Audience for this course

For department and program managers involved in building and/or implementing digital transformation within departments, measuring and improving business process efficiency, and responsible for implementing innovative technologies and optimizing production activities.

This course will also be useful for:

• Innovation and development managers responsible for identifying, evaluating, and implementing new digital solutions within the enterprise.

• Industrial safety and environmental experts interested in using AI to monitor emissions, control equipment condition, and improve industrial safety.

Outline

  1. Introduction to Artificial Intelligence (AI)
  • The Concept of Artificial Intelligence: Definition, Key Development Areas
  • The Difference Between AI and Traditional Automated Systems
  • General Classification of AI Technologies: Weak and Strong AI, Highly Specialized Systems
  1. Machine Learning and Neural Networks: Concepts, Differences, and Applications
  • What is Machine Learning: Operating Principles, Types of Learning
  • Neural Networks: Operating Principles, Architecture, and Applications
  • The Relationship Between Concepts: AI, Machine Learning, and Neural Networks
  1. Artificial Intelligence Tools
  • Overview of Modern AI Platforms and Solutions (including Generative AI, LLM, data analysis systems)
  • Predictive Tools
  • Computer Vision
  • Tools for Big Data Processing, Visualization, and Decision Making
  • Practical Practice – Case Study
  1. Prompt Engineering Basics
  • What is a Prompt and Why is it Needed?
  • Principles of Creating Effective Prompts for Solving Management Problems
  • Practical Examples: Report Preparation, Regulation Analysis, Decision Formulation, Working with Technical Documentation
  • Limitations and Risks of Using Generative AI
  • Practical Practice – Case Study
  1. Practical Application of AI in the Oil and Gas Industry industries.
  • Applicability of tools in various sectors: upstream, midstream, downstream
  • AI implementation cases in the oil and gas industry and related sectors, based on the experience of leading global companies
  1. Interactive practice and group assignments
  • Working with real-life scenarios similar to your company’s operations — discussion and hypothesis generation.
  • Case preparation and hypothesis development in groups (2 groups) with instructors
  • Analysis of generated cases (presentation of group results): identification of AI implementation opportunities, limitations and business impact, discussion
  • Discussion of strategic initiatives and barriers to digital transformation

 

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