Machine Learning Mastery Workshop – Oil & Gas Edition

Who should attend: scientists and engineers in the oil & gas industry who want to master machine learning techniques

What: a 3-day, highly interactive, application-driven machine learning workshop led by Enthought’s scientists who code. You’ll gain conceptual knowledge of machine learning models combined with intensive experience applying them to real-world oil & gas data and applications.

When: September 25-27, 2018 OR contact us with the form to the right to learn about bringing a private session to your organization

Where: Houston, TX (West Energy Corridor)

Cost: $1800. Discounts available for 3 or more attendees from the same organization.

Contact Us

Want to learn more? Ready to register? Fill out the form below or call 512.536.1057 to talk to our experts.

Interested in an onsite session for your group or organization?

Contact us with the form above for more details.

What is Machine Learning?

Machine Learning is a type of Artificial Intelligence (AI) that enables computers to learn to classify patterns and make predictions without being explicitly programmed.

Think of it as a set of data analysis methods that includes classification, clustering, and regression. These algorithms can be used to discover features and trends within the data without being explicitly programmed, in essence learning from the data itself.

How is it Used?
Machine learning is being used to enhance the capabilities and streamline the workflows of expert geoscientists and engineers. It can be used to speed up the classification, interpretation, and analysis of data; to process more diverse and complex data sets; and to discover hidden insights in the data.

Course Syllabus & Topics

Key Machine Learning Concepts
  • Linear and nonlinear models
  • Constant and variable learning-rates
  • Cost functions, regularization methods, and other constraints
  • Fitting, transforming, and predicting
Numeric Data
  • Logarithmic and curvilinear transforms
  • Data scaling
  • Outliers
  • Linear regressors
  • l1 and l2 normalization
  • Support vector machines (SVM)
Image Data
  • Image storage formats
  • Scikit-image
  • Smoothing and denoising
  • Edge detection
  • Feature-based segmentation
  • K-means clustering
Categorical Data
  • Contrast encoding
  • Missing values
  • Categorical rebinning
  • Linear classifiers
  • Tree-based classifiers
  • Ensemble methods
  • Boosting methods
  • Unbalanced designs
Putting it All Together: Applying Machine Learning to Specific Oil & Gas Problems

Using well logs to classify lithofacies

Analyzing well cuttings using dimensionality reduction and clustering

Classifying thin section images to identify different mineral types

Course Prerequisites

Knowledge of programming in the Python standard language (data structures, control flow, assignment, functions, and package access) and familiarity with array programming in NumPy is required. Familiarity with the Pandas and matplotlib libraries is also required (DataFrames, indexing, plot grids). Knowledge of general data analysis techniques and basic statistics (mean, standard deviation, correlation, etc.) is strongly recommended.

Individuals who have taken Enthought’s Python Foundations, Python for Scientists and Engineers, Python for Data Science, or Python for Data Analysis classes will have met the prerequisites for the course.

New to Python? No problem, contact us to learn about options that include the Python prerequisites.

Course Instructors

Enthought instructors have doctorates in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day. Enthought instructors possess professional, first-hand experience with the tools and technologies covered in our courses.