This class will focus on how to apply machine learning techniques to petrophysical and geological problems where students will walk away from the class with actual code and real data to start playing around with work-related problems. If you aren’t familiar with programming this is a great opportunity to learn and if you aren’t familiar with machine learning then you are about to discover it isn’t rocket science (when it works). There are limited spots available for this Workshop - book early to save your place.
Lewis Matthews was born and raised in the United Kingdom of Great Britain. At the age of 17 he emigrated to the USA and enlisted in the United States Navy where he served for 9 years as a Corpsman with Marines. Since then Lewis has received several degrees including economics, geology, and an MS in geophysics and seismology during which he independently discovered fractal clustering in petrophysical logs. He currently works for CrownQuest Operating as a data scientist where he evangelizes solutions to complex problems. To encourage understanding and broad collaboration across companies Lewis teaches machine learning applications for oil and gas problems. These workshops have proven to be incredibly popular and helpful to enhance the general understanding of the strengths and limits of these incredibly hyped technologies.
A flyer for this event is available.
The location for this event is Whiting Petroleum, 1700 Broadway Suite 900, Denver, Colorado.
Day 1 (May 8)
8:30-9:00am Registration & Breakfast (included)
9:00-11:00am Install and configure toolboxes
11:00-12:00pm Divide up into teams based on experience
12:00-1:00pm Lunch (included)
1:00-5:00pm Introduction to Machine Learning
Day 2 (May 9)
8:00-9:00am Breakfast (included)
9:00-12:00pm Applied Machine Learning
12:00-1:00pm Lunch (included)
1:00-5:00pm Team Assignments
Throughout the course of this jammed-packed two-days, you will cover a broad array of topics, which include:
Students are not expected to have experience and will be taught as though they've never written a program or taken a machine learning class. A student with reasonable programming knowledge, the student should be able to apply these workflows to work related petrophysical and geologic applications at the end of the second day. The language used is Python and there are several free toolboxes that will be used.
- Configure environment
- Introduction to Jupyter Notebook, Pandas, and Bokeh
- Introduction to Machine Learning
- Introduction to introduction to Scikit-Learn
- Introduction to KNN regression
- Introduction to SVM regression
- Introduction to Recursive Feature Elimination
- Introduction to Cross Validation
- Introduction to hyperparameter tuning
- Applied machine learning with petrophysics and EDR.
The students will:
(1) See real world examples of how operators are applying this technology to field operations while,
(2) gaining an appreciation of the domain complexity.
The skills acquired will vary depending on the level of experience. For beginners, students will gain basic machine learning skills they can build on. For ML experts, students will see cutting edge implementations in O&G they can build on.
Note: All the code and data is available for the students after the class. The dataset consists of 5 wells with open hole logs and electronic drilling dynamics. Students are free to research and publish, using the data provided under a creative commons license.
This Workshop includes two days of learning, breakfast, lunch, breaks and beverages. Attendees are responsible for bringing their own laptops with administrator rights. Early bird deadline has been extended to accommodate shrinking training budgets.
PPDM Members: $700 USD until May 1, 2019. $800 USD after May 1, 2019.
Non-Members: $900 USD until May 1, 2019. $1,000 USD after May 1, 2019. (Note: PPDM Individual memberships are only $100!)
Registration will close on May 7, 2019 at 5pm EST. For more information on this event, please contact email@example.com
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