Machine Learning in Oil and Gas: Challenges and Opportunities
ExxonMobil, the largest publicly traded international oil and gas company, uses technology and innovation to help meet the world’s growing energy needs. ExxonMobil’s Corporate Strategic Research (CSR) laboratory is a powerhouse in energy research focusing on fundamental science that can lead to technologies having a direct impact on solving our biggest energy challenges. The Data Analytics & Optimization Section at CSR is a multidisciplinary group of scientists conducting fundamental research in machine learning and mathematical optimization.
Use of advanced mathematical and computational models has historically been a major focus of research with applications in reservoir modeling and simulation, subsurface imaging, and refinery/chemical process modeling and optimization. In this talk, I will give an overview of the increasing role of data analytics and machine learning at ExxonMobil and some of the unique challenges and opportunities we face in applying these techniques to engineering and scientific data. We will also explore a couple of open research problems in more detail.
Niranjan Subrahmanya received B.S. and M.S. degrees in Mechanical Engineering from the Indian Institute of Technology, Bombay (2003), and a Ph.D. in Mechanical Engineering from Purdue University (2009). His dissertation focused on the development of machine learning algorithms for data-driven process modeling, monitoring and optimization. After graduation, he joined ExxonMobil’s Corporate Strategic Research Lab where he worked on adapting machine learning algorithms to various business problems. He is currently leading CSR’s research efforts in deep learning focusing on energy industry specific problems such as improving seismic interpretation and continuously optimizing complex field/refinery operations.