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

Author

Listed:
  • Minxing Si

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
    Tetra Tech Canada Inc., 140 Quarry Park Blvd Suite 110, Calgary, AB T2C 3G3, Canada)

  • Ling Bai

    (VL Energy Ltd., 208 Kincora Pt NW, Calgary, AB T3R 0A5, Canada)

  • Ke Du

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

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.

Suggested Citation

  • Minxing Si & Ling Bai & Ke Du, 2021. "Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1968-:d:497955
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    References listed on IDEAS

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    2. Brandt, Adam R. & Englander, Jacob & Bharadwaj, Sharad, 2013. "The energy efficiency of oil sands extraction: Energy return ratios from 1970 to 2010," Energy, Elsevier, vol. 55(C), pages 693-702.
    3. Christophe McGlade & Paul Ekins, 2015. "The geographical distribution of fossil fuels unused when limiting global warming to 2 °C," Nature, Nature, vol. 517(7533), pages 187-190, January.
    4. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
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