Short Courses


Short Courses

NOTE: Short-courses will be held after the conference and field trips at California State University Long Beach. Not included in registration for the general conference. Separate registration for a short course without paying general conference registration is possible.


Short Course #1: Introduction to Basin and Petroleum System Modeling

Leader: Allegra Hosford Scheirer, PhD, Research Scientist, Stanford University

Time and Date: 8:30am - 5:00pm, April 4 - 5, 2019
Cost: $300 for Professionals; $100 for Students

Summary: Course attendees will learn both the theory and practice of basin and petroleum system modeling. All lectures are accompanied with suggestions of both key readings and foundational texts. Outcrop photos and seismic expressions illustrate key concepts; case studies are essential to apply theory to practice; and effects of basin dynamics and sedimentation on petroleum systems weave through all modules. Class discussion is encouraged!


Short Course #2: Applications of Data Analytics and Machine Learning in Petroleum Geoscience

Leaders: Shuvajit Bhattacharya, PhD, University of Alaska, Anchorage; and Haibin Di, PhD, Schlumberger

Time and Date: 8:00am - 5:00pm, April 4, 2019
Cost: $300 for Professionals; $100 for Students

Summary: This course will provide a short, yet thorough, understanding of multivariate statistics and machine learning, and their applications in petroleum geosciences. Examples will cover various conventional and unconventional reservoirs, utilizing common and advanced well logs, and seismic data. Topics that will be covered include the following:
1. What is data analytics? Why should geoscientists care about it?
2. Geologic data types and dimensionality
3. Basic statistical measures (e.g. mean, standard deviation, and correlation coefficient etc.) for data visualization and analysis
4. What are machine learning and deep learning? Fundamentals of popular machine learning and deep learning algorithms (e.g. Multi-layer Perceptron and Convolutional Neural Network)
5. Working principles of supervised and unsupervised classification and regression techniques, and their utilities in constructing 3D facies, fault, and reservoir property models.



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