By International Petroleum Technology Conference (IPTC)
Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London
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 and geoscientists 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. Much of the standard terminology used in DL applications is also presented. In the afternoon, the on-line environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.
Who Should Attend?
This course, aimed at petroleum engineers and geoscientists, requires basic knowledge of Probability, Regression and Linear Algebra.
Module 1: Supervised vs Unsupervised Learning, Regression, Logistic Regression. Basic theory, terminology and examples.
Module 2: Feed-Forward Neural Networks and Convolutional Neural Networks. Basic theory, terminology and examples.
Module 3: Getting Started using open on-line resources: Python environment, DL libraries, software repositories, large datasets, useful websites, etc.
Module 4: Examples of successful DL industry applications.
Professor Olivier Dubrule is a Visiting Professor seconded by Total at the Department of Earth Science and Engineering at Imperial College London. He has spent most of his career developing new tools in relation with Geostatistics, Gaussian Processes and more recently Machine Learning. He is currently teaching the Machine Learning module of the new MSc Advanced Computational Science and Engineering at Imperial College. Olivier has been the 2003 SEG/EAGE Distinguished Instructor, and has taught many courses through the four IPTC Professional Societies in the last twenty-five years. He has authored two books, one of which has been translated in Russian and Farsi.
Lukas Mosser is a PhD Student in Machine Learning. Since he started his PhD in October 2016, Lukas has published important papers on Machine Learning in major peer-reviewed publications such as Transport in Porous Media and Physical Review Letters E. He specializes in the development of methods based on Generative Adversarial Networks (GANs) in relation to subsurface applications. Lukas has obtained numerous awards in the Machine Learning hackathons in which he recently competed.
Early Bird by 22 January 2019
Member: USD 695
Nonmember: USD 795
After 22 January 2019
Member: USD 795
Nonmember: USD 895
Email us for more information:firstname.lastname@example.org