Shale Analytics: AI-based Production Optimization in Shale
Disciplines: Data Science and Engineering Analytics | Reservoir
Data-driven analytics is becoming an important point of competitive differentiation in the upstream oil and gas industry. When it comes to production from shale, companies are realizing that they possess a vast source of important facts and information in their data. In analysis and modeling of production from shale, our traditional techniques leave much to be desired. Field measurement data can provide much needed insight. Data-driven analytics is the set of tools and techniques that provides the means for extraction of patterns and trends in data and construction of predictive models that can assist in decision-making and optimization.
In advanced data-driven analytics, data from the well and the formation are integrated with field measurements that represent completion and hydraulic fracturing practices and are correlated with production from each well. As the number of wells in an asset increases, so does the accuracy and reliability of the analytics.
Attendees will become familiar with the fundamentals of data-driven analytics and the most popular techniques used to apply them such as conventional statistics, artificial neural networks, and fuzzy set theory.
This course will demonstrate through actual case studies (and real field data from thousands of shale wells) how to impact well placement, completion, and operational decision-making based on field measurements rather than human biases and preconceived notions.
Basics of artificial intelligence (AI) and machine learning
- Impact of reservoir, completion, and operational characteristics on production
- Organize and prepare the data for predictive modeling
- Honor known physics of fluid flow in shale
- Avoid over-training (memorization) while promoting generalization
- Optimize completion practices
- Optimize well spacing and stacking
- Identify best service companies
Introduction to AI-based dynamic modeling
- Capture well and reservoir dynamics
- Address issues such as frac hits
1 or 2 Days
Application of data-driven analytics and predictive modeling in the oil and gas industry is fairly new. A handful of researchers and practitioners have concentrated their efforts on providing the next generation of tools that incorporates these technologies for the petroleum industry.
Data-driven analytics have become an integrated part of many new technologies used in our daily lives such as smart automatic transmissions in cars, the detection of explosives within airport security systems, smooth rides in complex subway systems, and the prevention of fraud in credit card use. They are extensively used to predict chaotic stock market behavior, and are increasingly being used to optimize financial portfolios.
A large amount of data is routinely collected during production operations in shale assets. The collected data can be utilized to gain a competitive advantage in optimizing production and increasing recovery.
This course is intended for completion engineers, production engineers and managers, reservoir engineers, geoscientists, asset managers, and team leaders.
0.8 or 1.6 CEUs (Continuing Education Units) are awarded for this 1- or 2-day course.
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).