Jan. 1, 2030


Description

This course is intended for oil and gas managers and professionals who want to learn about the digital transformation and the use of machine learning in oil and gas. The overall aim is to set you up for success in your data science, machine learning, artificial intelligence projects by giving an overall understanding of the field as applied to O&G.

We will begin with looking at digitization and the vision of the digital oilfield to set the scene. The basic technological ideas of machine learning, data science, and the preparation of data will be discussed. The role of domain knowledge and the process of judging whether results are good take a central role. We discuss various practical applications throughout O&G as well as how to assess and reap business value from these projects. We close by looking at project and change management and a brief overview of the toolsets available for this work.

This course is an overview and will not introduce any particular technique in detail. We will not compute anything or look at formulas or code. Rather, we will focus on understanding the key ideas and best practices that will make your project a success.


Featured Speakers

Speaker: Patrick Bangert
Speaker Patrick Bangert

Patrick heads the AI Engineering and AI Sciences teams at Samsung SDS America. He is responsible for Brightics AI Accelerator, a distributed ML training and automated ML product that is also included in the Brightics AI platform. He is responsible for X.insights, a data center intelligence platform. Among his other …

Patrick heads the AI Engineering and AI Sciences teams at Samsung SDS America. He is responsible for Brightics AI Accelerator, a distributed ML training and automated ML product that is also included in the Brightics AI platform. He is responsible for X.insights, a data center intelligence platform. Among his other responsibilities is to act as a visionary for the future of AI at Samsung.


Before joining Samsung, Patrick spent 15 years as CEO at Algorithmica Technologies, a machine learning software company serving the chemicals and oil and gas industries. Prior to that, he was assistant professor of applied mathematics at Jacobs University in Germany, as well as a researcher at Los Alamos National Laboratory and NASA’s Jet Propulsion Laboratory. Patrick obtained his machine learning PhD in mathematics and his Masters in theoretical physics from University College London.


Patrick co-chairs the SPE Digital Energy Technical Section (DETS) and publishes the quarterly magazine Digital Energy Review for DETS.


A German native, Patrick grew up in Malaysia and the Philippines, and later lived in the UK, Austria, Nepal and USA. He has done business in many countries and believes that AI must serve humanity beyond mere automation of routine tasks. An avid reader of books, Patrick lives in the San Francisco Bay Area with his wife and two children.

Full Description



Organizer

Patricia E. Carreras

Co-Chair SPE GCS Continuing Education Committee


Date and Time

Tue, Jan. 1, 2030

8 a.m. - noon
(GMT-0500) US/Central

Pricing

  • On demand recording
    $5.00
    (ends 01/01/2030)
Become a member

Location

Hyperlink to the recording will be provided after completing your registration.