Data Mining and Unsupervised Machine Learning in Canadian In Situ Oil Sands Database for Knowledge Discovery and Carbon Cost Analysis

A better understanding of greenhouse gas (GHG) emissions resulting from oil sands (bitumen) extraction can help to meet global oil demands, identify potential mitigation measures, and design effective carbon policies. While several studies have attempted to model GHG emissions from oil sands extractions, these studies have encountered data availability challenges, particularly with respect to actual fuel use data, and have thus struggled to accurately quantify GHG emissions. This dataset contains actual operational data from 20 in-situ oil sands operations, including information for fuel gas, flare gas, vented gas, production, steam injection, gas injection, condensate injection, and C3 injection.