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Tuesday, October 15 • 10:45am - 11:00am
Machine Learning and the Internet of Things Enable Steam Flood Optimization for Improved Oil Production

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Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. In contrast to traditional simulations of steam flood, a widely used enhanced oil recovery technique based on principles of classic physics, we introduce here an approach using cutting-edge machine learning techniques that have the potential to provide a better way to describe the performance of steam flood. We propose a workflow to address a category of time-series data that can be analyzed with supervised machine learning algorithms and IoT. We demonstrate the effectiveness of the technique for forecasting oil production in steam flood scenarios. Moreover, we build an optimization system that recommends an optimal steam allocation plan, and show that it leads to a 3% improvement in oil production. We develop a minimum viable product on a cloud platform.

avatar for Mi Yan

Mi Yan

Data Scientist, ExxonMobil
Mi Yan is a data scientist at ExxonMobil where he focuses on the application of machine learning in the oil and gas industry ranging from upstream to downstream. Mi received his PhD in physics from Rice University, and then served as a geophysicist at CGG. Later Mi joined Citibank... Read More →

Jonathan MacDonald

Imperial Oil Resources Ltd.

Chris Reaume


Wesley Cobb


Tamas Toth


Tuesday October 15, 2019 10:45am - 11:00am CDT
BRC 103

Attendees (3)