Introduction to Machine Learning for Petroleum Engineers [Kuala Lumpur]

21st September 2018 – Kuala Lumpur, Malaysia

Held in conjunction with the SPE Asia Pacific Digital Week Symposium.


In today’s competitive business environment, the traditional methods of data collection and analytics often aren’t enough. Professionals and companies alike are increasingly turning to powerful techniques from the Artificial Intelligence (AI) domain, a move spurred on by the recent leaps in computational power, better algorithms, larger amounts of data being collected, the availability of more advanced software libraries, and the adoption of easy to use programming languages. This short course explores the basic concepts and techniques used in Machine Learning and Neural Networks, some of the technology’s applications, and the need for data quality control.


  • Basic concepts of Machine Learning with an emphasis on oil and gas applications
  • Methods and algorithms used in Machine Learning and Neural Networks
  • Achieving a good understanding of the issues involved in the use of such techniques
  • A simple roadmap for the implementation of Machine Learning in a company
  • Discussions about data quality control to be used in Machine Learning
  • Understanding the key concepts for some algorithms currently used
  • Business cases to illustrate the concepts explored in the course

Pricing & Registration

SPE Members: USD 950 (Before Super Early Bird Date), USD 1000 (Before Early Bird Date) or USD 1100 (After Early Bird Date)
SPE Non-members: USD 1050 (Before Super Early Bird Date), USD 1100 (Before Early Bird Date) or USD 1200 (After Early Bird Date)

Learning Level

Introductory – Intermediate

Course Length

1 Day (Daily Activities Agenda)

Why Attend?

Upon completion of this course, participants are expected (to some extent) to:

  • Identify potential areas to implement Machine Learning techniques
  • Understand the basic concepts related to Machine Learning and Neutral Networks, as well as the main algorithms and their applications
  • Learn essential information about this new technology and how it can be used in the business context
  • Obtain insightful knowledge on how to implement AI tools and data QC
  • Consider the pros and cons of adopting new technology before “jumping on the bandwagon”
  • Learn the basic concepts of Machine Learning away from the complexities of mathematical and computer science
  • Avoid the use of new technologies in your business just for the sake of it
  • Compare different algorithms and applications commonly used in this area
  • Analyze in just a few steps how to implement a process to utilize Machine Learning

Who Should Attend

This course is intended for professionals interested in exploring the field of Data Analytics and improving drilling and related operations.

Professionals that are responsible in the following functional areas should attend:

  • Business Unit Head
  • Data and Business Analysts
  • Data Managers
  • Data Mining
  • Data Science Analysts
  • Drilling and Completions
  • Information Technology
  • Operations
  • Production Technology
  • Project Management
  • Reservoir Engineering
  • Risk Management


Engineers are responsible for enhancing their professional competence throughout their careers. Licensed, chartered, and/or certified engineers are sometimes required by government entities to provide proof of continued professional development and training. Training credits are defined as Continuing Education Units (CEUs) or Professional Development Hours (PDH). Attendees of SPE training courses earn 0.8 CEUs for each day of training. We provide each attendee a certificate upon completion of the training course.

In-House Training

This course is available for in-house training at your office location.

Instructor – Dr Carlos Damski

Dr. Carlos Damski has over 15 years of experience in drilling data analytics, dealing with major companies around the world through his research and as Chief Executive Officer of Genesis Petroleum Technologies, a company that provides solutions to improve drilling, completion, and workover processes in businesses of any size.

Dr. Damski has a PhD in Computer Science (Artificial Intelligence) from Sydney University and has had a number of technical papers published in OTC, SPE, IADC etc. He is the author of the book “Drilling Data Vortex – Where the bits meet the bits” which explains in detail how to use data to improve drilling activities in oil and gas.

Bringing extensive experience in software technology and drilling procedures, Dr. Damski is in the right position to model and extract business intelligence from complex drilling data, working directly with companies to improve their drilling processes and their bottom line.

Having delivered his “Data Analytics for Drilling Optimisation” course to SPE in the past, Dr. Damski is excited to work with professionals on the topic of Machine Learning and Neural Networks and spread the benefits of the latest technologies even further.