Published in the October 2016 edition of The Leading Edge magazine by the Society of Exploration Geophysicists. Read the full article here.

By Brendon Hall, Enthought Geosciences Applications Engineer 
Coordinated by Matt Hall, Agile Geoscience

ABSTRACT

There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist’s toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

One of the best examples is scikit-learn, a collection of tools for machine learning in Python. What is machine learning? You can 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.

Well logs and facies classification results from a single well.

In this tutorial, we will demonstrate how to use a classification algorithm known as a support vector machine to identify lithofacies based on well-log measurements. A support vector machine (or SVM) is a type of supervised-learning algorithm, which needs to be supplied with training data to learn the relationships between the measurements (or features) and the classes to be assigned. In our case, the features will be well-log data from nine gas wells. These wells have already had lithofacies classes assigned based on core descriptions. Once we have trained a classifier, we will use it to assign facies to wells that have not been described.

See the tutorial in The Leading Edge here.

ADDITIONAL RESOURCES:

• Enter the machine learning contest:  your mission, should you choose to accept it, is to make the best lithology prediction you can. We want you to try to beat the accuracy score Brendon Hall achieved in his Geophyscial Tutorial (The Leading Edge, October 2016). See the full contest details here.
• Learn more about Enthought’s geoscience solutions, including our Virtual Core and Canopy Geoscience software, as well as some of our consulting projects in the energy sector.
• Visit Enthought at Booth #2150  at the SEG Annual Meeting, October 17-19, 2016. in Dallas.