Abstract
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 greenhouse gas (GHG) emissions in the in situ oil sands industry. A low SOR indicates an extraction process that is more energy efficient and less carbon intensive. In this study, we applied machine learning methods for data-driven discovery to a public database, Petrinex, containing operating data from 2015 to 2019 extracted from over 35 million records for 20 in situ oil sands extraction operations. Two unsupervised machine learning methods, including clustering and association rules, showed that the cyclic steam stimulation (CSS) recovery method was less efficient than the steam-assisted gravity drainage (SAGD) recovery method. Chi-square tests showed a statistically significant association between the CSS recovery method and high SOR (p < 0.005). Two association rules suggested that the occurrence of non-condensable gas (NCG) co-injection produced a low SOR. Chi-square tests on the two rules identified a statistically significant relationship between gas co-injection and low SOR (p < 0.005). Association rules also indicated that there was no association between the production regions and SORs. For future in situ oil sands development, decision-makers should consider SAGD as the preferred method because it is less carbon intensive. Existing in situ oil sands projects and future development should explore the possibility of NCG co-injection with steam to reduce steam consumption and consequently reduce GHG emissions from the extraction processes.
Highlights
To keep the average global temperature rise below 2 ◦ C, a third of global oil reserves have to remain undeveloped [1]
Machine learning methods for data-driven discovery were applied to a public database, Petrinex, containing operating data from 2015 to 2019 that were extracted from over 35 million records for 20 in situ oil sands extraction schemes
The use of clustering and association rules and two unsupervised machine learning methods implied that: (1) the cyclic steam stimulation (CSS) recovery method was less efficient than steam-assisted gravity drainage (SAGD) recovery as schemes proceed toward maturity (Rule 5); (2) gas co-injection resulted in low steam oil ratio (SOR)
Summary
To keep the average global temperature rise below 2 ◦ C, a third of global oil reserves have to remain undeveloped [1]. In 2019, Canada was the fourth largest oil producer, contributing 5% to the global oil production [2], and had the third largest proven oil reserves (following Venezuela and Saudi Arabia) with over 167 billion barrels (bbls) [3]. Masnadi et al [4] reported that Canada was the fourth highest carbon-intensive upstream oil producer in the world, after Algeria, Venezuela, and Cameroon. This is because over half of the oil production in Canada comes from an unconventional oil resource called oil sands. The heavy oil separated from the oil sands is called bitumen, which contains particulate organic material, hydrocarbons, associated metals, and sulphur compounds [6]. Almost all oil sands reserves in Canada are concentrated in the Athabasca, Cold Lake, and Peace River regions in Northern Alberta
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
