SC-02 Advanced Data Analytics & Machine Learning for Energy Professionals
Sunday, 8 June 2025, 8:00 a.m.–5:00 p.m. | Houston, Texas
Who Should Attend
Reservoir and petroleum engineers interested in data-driven approaches.
Energy professionals working with subsurface data and production forecasting.
Data scientists and analysts transitioning into energy applications.
Technical managers seeking to understand advanced analytics applications in energy operations.
Anyone with a foundational knowledge of machine learning looking to apply it meaningfully within the energy sector.
Course Content
This course focuses on the advanced application of data analytics and machine learning to energy industry data.It is a critical step in laying the foundation necessary for thinking statistically and identifying key signals amid the noise that is data.
This course will teach you:
- To effectively prepare data for deep dives with advanced analytic techniques and ensure that any conclusions drawn are trustworthy and reliable
- To glean insights and make predictions from your data using techniques such as outlier detection, data debiasing and imputation, feature engineering, anomaly detection, supervised and unsupervised learning, spatiotemporal modeling, and uncertainty modeling
- To understand the assumptions and limits of data precision, scale and coverage, spatial interpolation, multivariate models, analytics, and uncertainty models—given that predictions are only as strong as your process
- To critically evaluate your models with model checking and explainable artificial intelligence
Why Attend: This course offers a unique opportunity to sharpen your analytical skills with a hands-on, energy-specific focus. Led by a leading expert in the field, it dives deep into practical techniques like supervised/unsupervised learning, anomaly detection, uncertainty modeling, and spatial analysis. Attendees will walk away with stronger modeling practices, better decision-making capabilities, and a clear understanding of how to apply cutting-edge machine learning techniques in real-world reservoir engineering and subsurface data scenarios.
Disciplines: Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir
Learning Level: Intermediate to Advanced
Special Requirements
- Laptop Required: Please bring a laptop to participate in hands-on exercises during the course.
- Software Installation: Prior to class, download and install Anaconda Python. Instructions are provided in your pre-course materials.
Instructor
Michael Pyrcz Michael Pyrcz is a professor in the Cockrell School of Engineering, and the Jackson School of Geosciences, at The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also the principal investigator of the Energy Analytics freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin, an associate editor for Computers and Geosciences, and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences. Michael has written over 70 peer-reviewed publications, a Python package for spatial data analytics, co-authored a textbook on spatial data analytics, ‘Geostatistical Reservoir Modeling’ and author of two recently released e-books, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy and Applied Machine Learning in Python: a Hands-on Guide with Code.
All of Michael’s university lectures are available on his YouTube channel with links to 100’s of Python interactive dashboards and well-documented workflows on his GitHub account, to support any interested students and working professionals with evergreen content. To find out more about Michael’s work and shared educational resources visit his website, www.michaelpyrcz.com.
YouTube Channel: www.youtube.com/GeostatsGuyLectures
GitHub Repositories: https://github.com/GeostatsGuy/
Free e-books:
Machine Lerning in Python: https://geostatsguy.github.io/MachineLearningDemos_Book
Geostatistics in Python: https://geostatsguy.github.io/GeostatsPyDemos_Book
Fees
Room Assignment: Room 371A
Registration Fees:
Early Bird
on or before 9 May 2025 | Onsite
after 10 May 2025 |
Member $550
Nonmember $750
Student $275 | Member $750
Nonmember $950
Student $375 |
Attendee Limit: 40
Educational Credits: .8 CEUs/ 8 PDH's will be awarded for attending this 1-day course
Venue
George R. Brown Convention Center
1001 Avenida De Las Americas
Houston,
Texas
77010
United States