Courses

About this course

Interest in data science and machine learning is rapidly expanding, offering the promise of increased efficiency in E&P, and holding the potential to analyze and extract value from vast amounts of under-utilized legacy data. Combined with petroleum geoscience and engineering domain knowledge, the key elements underlying the successful application of the technology are: data, code, and algorithms. This course builds on public datasets, code examples written in Python, statistical graphics, and algorithms from popular data science packages to provide a practical introduction to the subject and its application in the E&P domain

This course will be delivered entirely online, for 6 hours each day with 1 hour break.

What you’ll learn

The course comprises a mix of lectures and hands-on computer workshops. You’ll gain a working knowledge of coding in Python. You’ll learn the tradecraft of data import and manipulation, data visualization, exploratory data analysis, and building predictive models from data. You’ll also gain a powerful working environment for data science on your own computer, which together with code examples provided by the course will give you a jump start to applying the techniques you’ll learn to your own projects. For a flavor of what you’ll learn, check out this gallery of visualization samples drawn from the course workshops.

What data sources are used?

Using real E&P data sources is an important element of the hands-on computer workshops. This course makes extensive use of open data provided the UK Oil and Gas Authority and the UK National Data Repository. These data sources are not only typical of the challenges and complexity presented by E&P datasets, but also contain sufficient data quality issues to make them ideal for teaching the all important skills of data cleaning and manipulation. The course makes use of well logs, tops, seismic, and production data from these sources. The data are released in the public domain and you can continue to use these sources as you gain in experience after the course.

What data science tools are used ?

The course introduces a data science toolkit based on Visual Studio Code from Microsoft. This free product is rapidly growing in popularity as an environment for Python coding and data science. We think this toolkit provides a best-in-class environment for learning data science and subsequently moving to work on real projects, and we provide a free extension to further enhance it’s data science capabilities. More information about our data science extension is available here. The toolkit components will be installed on your computer - the advantage of this approach over cloud-based platforms is that your data is never uploaded to the cloud (if security is an issue), and you will be able to continue working when offline (if internet access is an issue).