Predicting the Dynamics Driving US Natural Gas Liquid (NGL) Waterborne Exports
Team Members: Leslie An, Yunda Jia, Michael Sptintson, Yuetong Yang, Yue Zhuo
This project applies statistical machine learning methods to identify primary driver(s) behind US propane and butane exports from the Gulf Coast and to develop a model to aid analysts in predicting those exports given world pricing and export data for the LPG market. The results may help Energy Transfer predict changes in markets more efficiently and identify pricing strategies to capitalize on them, which will lead to higher returns.
Sponsor: Energy Transfer
Sponsor Mentor: Tony Pule
D2K Fellow: Jasper Tan
Midcontinent Business Unit Pumping Health: Predictive Maintenance
Team Members: Sara Bolf, Julia Coyner, Henry Creamer, Alexander Kalai, Kuida Liu, Kevin Ong
The project is to use daily well scan data and work-order data from Chevron to create a model that determines whether a pumping unit requires maintenance, which will later be used to create a predictive maintenance model to decrease non-producing time on these wells.
Sponsor: Chevron Corporation
Sponsor Mentor: Kristine Hu
D2K Fellow: Daniel LeJeune
Natural Language Processing in Detecting Emerging Topics in Health and Environmental Science
Team Members: Yixiao Li, Esther Lim, Vladimir Belik, Siyu Guo, Josh Dunning, Patrick Chickey
The goal of the project is to create a tool to identify the emerging topics in health and environmental science so that ExxonMobil can effectively protect people and the environment on the most scientifically accurate understanding of emerging issues.
Sponsor Mentor: Rafael D. Moas
D2K Fellow: Jack Wang