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.
AI & ML Applications in Oil and Gas Industry
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AI & ML Applications in Oil and Gas Industry
This course is part of Petroleum Engineering with AI Applications Specialization
Instructor: Subject Matter Expert
Included with 
Gain insight into a topic and learn the fundamentals.
5.0
(26 reviews)
Beginner level
Recommended experience
5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Skills you'll gain
- Advances in AI Technology for O&G Industry
- Data handling in O&G Industry
- Onshore and Offshore Hydrocarbon Storage Facilities
- Seismic Data Processing using AI & ML
- Overview of crude oil treating systems
- Geomodelling Process
- ML in Reservoir Engineering
- Overview of natural gas production and processing
- Overview of oil and gas exploration and drilling methods
Details to know
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Recently updated!
August 2024
Assessments
2 assignments
Taught in English
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This course is part of the Petroleum Engineering with AI Applications Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
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.
What's included
14 videos1 assignment
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.
What's included
15 videos1 assignment
Instructor
Offered by
Recommended if you're interested in Mechanical Engineering
University of Michigan
EIT Digital
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