Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations

Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations

Canada’s in situ oil sands can help meet the global oil demand. Because of the energy-intensive extraction processes, in situ oil sands operations also play a critical role in meeting the global carbon budget. The steam oil ratio (SOR) is an indicator used to measure energy efficiency and assess gre...

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Bibliographic Details
Main Authors: Minxing Si, Ling Bai, Ke Du
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Sustainability
Subjects:
in situ oil sands
data mining
Petrinex
k-means
unsupervised machine learning
clustering
Online Access:https://www.mdpi.com/2071-1050/13/4/1968

Internet

https://www.mdpi.com/2071-1050/13/4/1968

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