SPE Petroleum Data Analytics Series - Week one: Subsurface Analytics
Disciplines: Data Science and Engineering Analytics | Reservoir
Petroleum Data Analytics is the application of Artificial Intelligence and Machine Learning in the oil and gas industry. Future of our industry will be highly influenced by Petroleum Data Analytics. Engineering-domain experts who become highly skilled AI and Machine Learning practitioners will be controlling the future of engineering disciplines including petroleum engineering. Becoming an engineering-related AI and ML expert practitioner requires fundamental understanding and extensive experience of using AI and Machine Learning to solve engineering-related problems.
The objective of this week-long course is to provide the required foundation and the realistic engineering applications of AI and Machine Learning to the new generation of petroleum professionals that have recognized the future potential impact of AI and Machine Learning in our industry. It is obvious that short courses will not cover and provide all that is necessary for petroleum professionals to become true Petroleum Data Analytics Experts. However, it is a fact that a week, such as this, will play a crucial role for the enthusiasts of this technology to identify the scientific realities associated with the foundation of Artificial Intelligence and Machine Learning and its true application in Petroleum Data Analytics.
Topic Covered in Week One: Subsurface Analytics
- Day 1: Artificial Intelligence & Machine Learning – Theoretical Background
- Day 2: Python for Petroleum Data Analytics
- Day 3: Shale Analytics – Completion and Production Optimization using AI & Machine Learning
- Day 4: Reservoir Analytics – Data-Driven Reservoir Simulation and Modeling
- Day 5: Reservoir Analytics – Enhancing Numerical Reservoir Simulation using AI & ML
Brief Course Outline
Day 1: Artificial Intelligence & Machine Learning – Theoretical Background
- Brief History and Definitions
- Artificial Neural Networks
- Fuzzy Set Theory
- Evolutionary Computing
Day 2: Python for Petroleum Data Analytics
- Basics of Computer Programming
- Python for Data Analytics
- Coding AI and Machine Learning in Python
- AI-based Application Development using Real Data from North Sea
Day 3: Shale Analytics – Completion and Production Optimization using AI & Machine Learning
- Descriptive Analytics (Field Measurement Analyses)
- Predictive Analytics (Fact-Based, Validated Modeling of Completion and Production)
- Prescriptive Analytics (Completion and Production Optimization)
- Modeling Frac-Hits (Introduction to Dynamic Shale Analytics)
Day 4: Reservoir Analytics – Data-Driven Reservoir Simulation and Modeling
- Top-Down Modeling
- Development of Spatiotemporal Databases
- Automated History Matching
- Reservoir Model Validation in Space and Time
- Field Development Planning and Reservoir Management
Day 5: Reservoir Analytics – Enhancing Numerical Reservoir Simulation using AI & ML
- Smart Proxy Modeling
- Data Requirements from Numerical Simulation Models
- Smart Proxy Validation using Blind Simulation Runs
- Field Development Planning and Reservoir Management
- Application to Computational Fluid Dynamic (CFD)
This course will play a crucial role for the enthusiasts of this technology to identify the scientific realities associated with the foundation of Artificial Intelligence and Machine Learning and its true application in Petroleum Data Analytics. Want to be knowledgeable with the most up-to-date and accurate AI and Machine Learning technology? This class will get you there!
This course is intended for all Petroleum engineering professionals, geoscientists, managers, geoscientists, asset managers, and team leaders.
Students will want to bring a calculator as well as their laptop.
4 CEUs (Continuing Education Units) are awarded for this 5-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.
Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.
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).