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DAT 600

Advanced Petroleum Data Analytics

This introductory course focuses on petroleum data types (such as field data, lab data, and simulated data) and data analytics tools available today in the context of these data types.

COURSE SCHEDULE

Code Date Location price (€)*
DAT 600 8 - 9 May 2025 Online 850
DAT 600 2 - 3 Jul 2025 Online 850
DAT 600 9 - 10 Apr 2025 Stavanger 1990
DAT 600 4 - 5 Jun 2025 Abu Dhabi 1990

* Prices are subject to VAT and local terms. Ph.D. students, groups (≥ 3 persons) and early bird registrants (8 weeks in advance) are entitled to a DISCOUNT!

COURSE OVERVIEW

COURSE OUTLINE

2 days
Day 1

o Introduction and Course Agenda
o Basic types of petroleum data
o Short group exercise in identifying various data types
o AI algorithms introduction
o AI: Evolutionary computation algorithms with application examples
o Exercise to apply EC to an E&P problem

Day 2

o AI: Swarm Intelligence algorithms with application examples
o Exercise to apply SI to an E&P problem
o Machine learning and most popular ML models. Application examples from E&P industry
o Fuzzy logic and its hybridization with ML models, examples
o Information theory and Akaike Information Criterion uses in
petroleum E&P problems
o Discussion
o Adjournment

INSTRUCTOR

Dr. Tatyana Plaksina

FAQ

DESIGNED FOR

This course is designed for practicing petroleum engineers, geologists, geoscientists, and petroleum decision makers who would like to familiarize themselves with the most popular data analytics tools and learn about value that they add to business, operational workflows, and decision making.

COURSE LEVEL
  •  o Beginner to Intermediate

LEARNING OBJECTIVES

Some of the learning objectives of this course include:
o  Familiarize and learn to distinguish various basic types of petroleum data
o Learn to match various data types and data analytics tools and from this standpoint choose appropriate tools for outstanding engineering problems
o  Learn to distinguish various data analytics tools and familiarize with their conceptual structure
o Consider multiple popular AI, ML, fuzzy logic, and information theory algorithms and gain deeper understanding of how they were applied to solve various engineering problems (though presented examples and discussion).

REGISTER

Registration is now OPEN!

* Prices are subject to VAT and local terms. Ph.D. students, groups (≥ 3 persons) and early bird registrants (8 weeks in advance) are entitled to a DISCOUNT!

For more details and registration please send email to: register@petro-teach.com

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