Nov. 11, 2021


Description

The objective of this training event is to learn the theory and practical application of machine learning within the Energy industry to help improve data-driven decision making.

 A brief description of the training follows:

Introduction

  • Introduce topics and overview of Subsurface Machine Learning.

Introduction to Machine Learning

  • Provide definitions, fundamental concepts of inference and prediction along with the opportunity and limitations of machine learning.

Inference

  • Learn about the system with limited observations. Sampling bias, Feature Selection, and more.

Prediction

  • Motivation and methods for predictive machine learning methods including hyper parameter tuning with k nearest neighbors.

Machine Learning Examples

  • Real world applications of Subsurface Machine Learning.

Featured Speakers

Speaker: Dr. Michael Pyrcz
Speaker Dr. Michael Pyrcz

Dr. Michael Pyrcz is Co-founder and Chief Science Officer at daytum.
He is also an Associate Professor in the Cockrell School of Engineering and the Jackson School of Geosciences at the University of Texas at Austin. Dr. Pyrcz teaches and conducts research on spatiotemporal statistical modeling, data analytics, geostatistics, and machine …

Dr. Michael Pyrcz is Co-founder and Chief Science Officer at daytum.


He is also an Associate Professor in the Cockrell School of Engineering and the Jackson School of Geosciences at the University of Texas at Austin. Dr. Pyrcz teaches and conducts research on spatiotemporal statistical modeling, data analytics, geostatistics, and machine learning. Known in the online community as “The Geostats Guy”, his main objectives are to demystify machine learning and find practical applications within the subsurface.


With over 18 years of experience in consulting, teaching and industrial R&D in statistical modeling, reservoir modeling and uncertainty characterization, he is able to teach and work with both engineers and geoscientists alike.


Dr. Pyrcz believes in a digital transformation in the energy where we can do more with our data and machine learning.

Full Description



Organizer

Patricia E. Carreras - Chair, SPE GCS Continuing Education Committee

Debora Martogi Simanjuntak - Secretary, SPE GCS Continuing Education Committee


Date and Time

Thu, Nov. 11, 2021

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

Pricing

  • SPE Member
    $45.00
    (ends 11/08/2021)
  • SPE MiT - Retired - Student
    $20.00
    (ends 11/08/2021)
  • Non SPE Member
    $55.00
    (ends 11/08/2021)
Become a member

Location

Online - No recording