Energy & Safety

Detection, Characterization, and Quantification of Stick-Slip in Hydraulic Systems

Stick-slip is when hydraulic systems such as elevators start shaking erratically, which can be dangerous, and currently cannot be measured! Our project is to engineer a measurement system for the amount of stick-slip in pistons, a key piece of hydraulic equipment, so Shell Hydraulics can test different ways to eliminate stick-slip.

Stribeck Curve FDA Project

From large industrial equipment, to inside the engines of our personal cars, lubricants are crucial to allow for efficient, smooth, and cool operation. Using statistics and data science, the project aimed to quantitively compare lubricants to determine which was best for a given scenario.

Rice D2K Lab students build a predictive maintenance model to improve operations with machine learning.

Do you know that unexpected shutdowns in the natural gas compression industry cost millions of dollars every year? Texas is an energy-powered state, where having a reliable power grid impacts everyone’s bottom line.

Five Rice engineering students, enrolled in the data science projects capstone offered by the D2K Lab and sponsored by D2K Lab Affiliate member CSI Compressco, worked with compressor data sets and used predictive modeling to prevent costly shutdowns. Team CS I Predict competed at the end of semester Spring 2021 D2K Showcase and won first place.

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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: ExxonMobil

Sponsor Mentor: Rafael D. Moas

D2K Fellow: Jack Wang

Interested in working with Rice students and faculty on a real-world data science project? Send us an email at

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