Petroleum Data Analytics - Engineering Application of Artificial Intelligence & Machine Learning
Disciplines: Completions | Production and Operations | Reservoir
Artificial Intelligence and Machine Learning is revolutionizing many industries. This technology is becoming an important point of competitive differentiation in the upstream oil and gas industry. Since optimization of production and enhanced recovery is the important issue for the petroleum industry, companies are realizing that reality and actual field measurements play much more important role in success of decision making than traditional assumptions, interpretations, and preconceived notions.
“Data” representing the actual field measurements, can provide much needed insight. Petroleum Data Analytics provides a set of tools and techniques for both conventional and unconventional resources to extract patterns and trends from data and construct predictive models to assist decision making and optimization. The most important part of Engineering Application of Artificial Intelligence and Machine Learning in the Petroleum Industry is the realistic and scientific understanding of this technology, its differences with traditional statistics and its non-engineering applications. This short course covers all such details to generate the interest in becoming Petroleum Data Analytics Engineers.
- Basics of Artificial Intelligence (AI) and Machine Learning (ML)
- Artificial Neural Networks (Deep Learning)
- Fuzzy Set Theory
- Evolutionary Computation
- Engineering & Non-Engineering Problem Solving using AI&ML
- Traditional Statistics versus AI&ML
- Petroleum Data Analytics
- Conventional Resources
- Data-Driven Reservoir Modeling - Top-Down Modeling (TDM)
- Actual Case Studies
- Conventional Resources
- Unconventional Resources
- Shale Analytics
- Multiple Case Studies
- Shale Analytics
Petroleum Data Analytics is fairly new. A handful of domain experts have dedicated extensive amounts of time and effort to develop and present the next generation of tools that incorporate these technologies in the petroleum industry. Unfortunately, hypes, buzz words, and marketing ploys around data analytics have overwhelmed the petroleum industry in the past few years, specifically in the United States and most recently throughout the world. Many with little to no understanding and knowledge of the physics and the geology and other with very superficial understanding of AI and Machine Learning have been marketing these hypes.
This course will demonstrate the power of Artificial Intelligence and Machine Learning and the difference this technology can make for informed decision making when it comes to accomplishing important short-term, mid-term and long-term objectives. This course will also show how to distinguish between realistic application of AI and Machine Learning versus marketing ploys.
This course is designed for geo-scientists, engineers, and managers. Specifically, those involved with geology, drilling, reservoir, completion, and production in operating and service companies. In general, those involved in planning, completion, and operation in assets are the main target audience.
All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.
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SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.
We reserve the right to substitute course instructors as necessary.
Dr. Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).
Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE’s Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011).
He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).