Advanced Data Analytics & Machine Learning for Energy Professionals

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Disciplines: Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir

Course Description

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

Learning Level

Intermediate to Advanced

Course Length

1-Day

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.

Who Attends

• 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

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.

CEUs

.8 CEU's/8 PDH's are awarded for this 1-day class

Additional Resources

To enhance your learning experience, the instructor has provided the following supplemental materials, which are free and publicly accessible:

YouTube Channel – GeostatsGuy Lectures:
www.youtube.com/GeostatsGuyLectures

GitHub Repositories – Source code, demos, and notebooks:
https://github.com/GeostatsGuy/

Free E-books:
• Machine Learning in Python – https://geostatsguy.github.io/MachineLearningDemos_Book
• Geostatistics in Python – https://geostatsguy.github.io/GeostatsPyDemos_Book

Cancellation Policy

All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.

Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.

SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.

We reserve the right to substitute course instructors as necessary.

Instructor