Petroleum Data Analytics (DAT 606)
Petroleum Data Analytics (DAT 606)
COURSE SCHEDULE
| Code | Date | Location | price (€)* |
|---|---|---|---|
| DAT 606 | 1 - 5 Jun 2026 | Stavanger | 4400 |
COURSE OVERVIEW
Petroleum Data Analytics (PDA) refers to the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques within the oil and gas industry. The future of the industry will be significantly shaped by advances in PDA. Engineering professionals who develop strong expertise in AI and ML applications will play a leading role in the evolution of petroleum engineering and related disciplines.
Becoming an effective AI and ML practitioner in an engineering context requires a solid understanding of fundamental principles, as well as practical experience applying these tools to real-world technical problems.The objective of this weeklong course is to provide realistic and practical foundations in Petroleum Data Analytics for the new generation of petroleum professionals who recognize the transformative potential of AI and ML. While no short course can fully develop complete expertise in this field, this intensive program offers a critical starting point. It helps participants understand the scientific principles underlying AI and ML and how these methods can be meaningfully and responsibly applied to petroleum engineering challenges.
COURSE OUTLINE
5 days
Day 1
o Definitions and brief history
o Modeling physics using AI
o Engineering Application of AI
o Traditional statistics versus AI
o Hybrid models
Day 2
o Artificial neural network
o Fuzzy set theory
o Evolutionary computing
o Explainable AI
o AI-Ethics
Day 3
o Top-down modelling
o Geo-Analytics: AI-base geological modelling
o Actual case studies
Day 4
o Traditional proxy modelling
o Smart proxy modelling
o AI-based carbon capture & storage
Case studies
Day 5
o Shale analytics
o Case studies
o AI-based modelling of Frac-Hit
o AI-based Production Allocation of Stages/Cluster via Fiber Optics
INSTRUCTOR
Instructor Profile
Instrucor is a pioneer in the application of Artificial Intelligence (AI), Machine Learning, and Data Mining in the Exploration and Production industry. He is Professor of Petroleum and Natural Gas Engineering at West Virginia University and President and CEO of Intelligent Solutions, Inc. (ISI).
He holds B.S., M.S., and Ph.D. degrees in Petroleum and Natural Gas Engineering. He has authored three books—Shale Analytics, Data-Driven Reservoir Modeling, and Application of Data-Driven Analytics for the Geological Storage of CO₂—as well as more than 170 technical papers. He has led over 60 projects for independent operators, national oil companies (NOCs), and international oil companies (IOCs). An SPE Distinguished Lecturer, he has been featured four times as Distinguished Author in SPE’s Journal of Petroleum Technology (JPT). He is the founder of Petroleum Data-Driven Analytics, SPE’s technical section dedicated to AI, machine learning, and data mining. He was honored by the U.S. Secretary of Energy for his technical contributions following the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and served on the U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources (2008–2014). He also represented the United States at the International Organization for Standardization (ISO) on Carbon Capture and Storage (2014–2016).
FAQ
DESIGNED FOR
This course is designed for Petroleum engineers and geoscientists as well as managers and decision makers in NOCs, IOCs, Independents, and Service Providers. In general, those involved in planning, and decision making of hydrocarbon assets are the main target audience.
COURSE LEVEL
o Intermediate to Advanced
LEARNING OBJECTIVES
This course will demonstrate the power of Artificial Intelligence and Machine Learning and the difference they 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.
REGISTER
Registration is now OPEN!
Ph.D. students, group and early bird registrants are eligible to DISCOUNT!
For more details and registration please send email to: register@petro-teach.com
PREREQUISITES
Understanding of petroleum engineering concepts. Attendees should have petroleum engineering background or at least five years of working experience in the industry.
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