Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers are interested to know more about DL, but are not sure where to start. This one-day course aims to provide this introduction.
The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. In the afternoon, the environment associated with DL is discussed, from Python libraries to software repositories, important websites and big datasets available on the Web.
The last part of the course is spent discussing the most promising subsurface applications of DL.
This course, aimed at petroleum engineers and geoscientists, requires basic knowledge of Probability, Regression and Linear Algebra.
AGENDA:
9.00-10.15: Supervised vs Unsupervised Learning, Regression, Logistic Regression. Basic theory and examples.
10.15-10.45: Break
10.45-12.15: Fully Connected Neural Networks and Convolutional Neural Networks. Basic theory and examples.
12.15 – 13.15am Lunch
13.15-14.30: Getting Practical: large existing datasets, websites, Python Deep learning libraries
14.30-15.00: Break
15.00 -16.45: Four Deep Learning Industry Applications.
16.45-17.00: Conclusion
Venue: The event will be held at the Department of Earth Science and Engineering, Imperial College London. Map available here.
Directions: Please note the main entrance to the Department is via the Royal School of Mines Building on Prince Consort Road, between 10 and 12 on the campus map
Booking: All booking must be paid in advance and online please, via Eventbrite.
Email: katespe@aol.com
Costs: (Booking fee applies)
Early Bird: 199 before September 15
SPE/PESGB/EI members: 245
Non-members: 285