Introduction to Machine Learning for Petroleum Engineers
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
Introductory - Intermediate
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
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
- Production Technology
- Project Management
- Reservoir Engineering
- Risk Management
0.8 CEUs (Continuing Education Units) are awarded for this 1-day course.
This course is available for in-house training at your office location.
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We reserve the right to substitute course instructors as necessary.
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.