Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!

Grab it

This course, “AI&ML Applications in Oil and Gas Industry” takes into a comprehensive journey through the oil and gas industry, exploring both the fundamental overview and the cutting-edge applications of Artificial Intelligence and Machine Learning (AI&ML). It gives a holistic understanding of the industry's core principles, while uncovering the transformative potential of AI&ML technologies in revolutionizing operations and decision-making processes. This course begins with an in-depth exploration of the origin and formation of crude oil, types of reservoirs and the entire life cycle of oil and gas fields. It reveals the intricacies of oil and gas exploration methods, drilling processes, gathering stations and surface production/separation facilities. It covers the essentials of crude oil treating systems, natural gas processing and onshore/offshore hydrocarbon storage facilities. This course delves into the forefront of AI&ML applications within the oil and gas industry. It explains how ML techniques enhance seismic data processing, geomodelling and reservoir engineering, enabling accurate reservoir characterization and optimal production engineering. It also explores the vast potential of AI in the upstream sector, revolutionizing exploration, drilling and production optimization. It keeps the learners updated on the latest advances in AI technology and big data handling, empowering them to drive innovation and efficiency in the industry. Target learners: Students pursuing Diploma / UG / PG Programs in Chemical/ Petroleum/ Oil and Gas Engineering. Faculties / Working Professionals in the above domain & other aspiring learners. Prerequisite: Basic Chemical/ Petroleum/ Oil and Gas Engineering with Basic Python knowledge.

Syllabus

Overview of Oil and Gas Industry

The module on "Overview of Oil and Gas Industry" provides students with a comprehensive understanding of the fundamentals and key components of the oil and gas industry. It begins with an exploration of the origin and formation of crude oil, enabling students to grasp the geological processes that lead to its existence. It further discusses elaborately the accumulation and types of reservoirs, gaining insights into the various geological formations that contain oil and gas. The module provides an overview of the life cycle of oil and gas fields, covering exploration methods used to identify potential reserves. It also explores the equipment used in upstream oil and gas production, including drilling rigs, gathering stations and surface production/separation facilities. Furthermore, it introduces crude oil treating systems and natural gas processing, understanding the processes involved in refining and purifying these resources. The module concludes with an exploration of onshore and offshore hydrocarbon storage facilities, highlighting their importance in the oil and gas industry.

AI&ML Applications in Oil and Gas Industry

The module on "AI & ML Applications in the Oil and Gas Industry" explores the transformative role of artificial intelligence and machine learning in revolutionizing the operations and decision-making processes within the industry. The module begins with a comprehensive review of the impacts of ML in the oil and gas industry, highlighting the key advancements and benefits brought about by these technologies. It further delves into specific applications, starting with seismic data processing techniques, focusing on salt body delineation. It also explores the geomodelling process and its integration with AI and ML algorithms for accurate reservoir characterization. Reservoir engineering will be a key focus, with emphasis on ML techniques for reservoir rock classification and optimal production engineering. It gives insights into the application of AI in the upstream sector of the industry, exploring its use in exploration, drilling and production optimization. Advances in AI technology specific to the oil and gas industry is covered, including the integration of machine learning with sensor data, predictive maintenance and anomaly detection. It gives a fundamental understanding of data handling in the industry and the state-of-the-art approaches to handle big data in the oil and gas domain.