1887
Volume 31, Issue 4
  • ISSN: 1354-0793
  • E-ISSN:

Abstract

Thermal-compositional flow simulations are crucial for understanding the intricate interactions of subsurface heat and mass transfer for geoenergy development. A case in point is the steam-assisted gravity drainage (SAGD) process, where accurate numerical simulation is essential to evaluate its efficiency and ensure environmentally sustainable oil development. However, representing the complex heat and mass transfer in SAGD requires fine-scale grids, leading to exceptionally high computational costs. To address this challenge, this study introduces a novel upscaling technique that enables efficient SAGD modelling with larger grid sizes while maintaining simulation accuracy. The proposed upscaling method employs scale factors, defined as the ratio of the coarse-scale grid sizes to the fine-scale grid sizes, to adjust the oil viscosity–temperature relationships in coarse-scale models. This method saves the need to modify the underlying source code of simulators, and thus favours the users of closed-source commercial modelling software, enabling more efficient and cost-effective field-scale SAGD simulations. The method is validated on the SAGD models of different dimensions, grid-block and overall model sizes, and oil viscosity–temperature relationships. The results show that the upscaling method speeds up the fine-scale simulations to 3.6 and 7887 times faster for 1D and 2D SAGD models, respectively, while preserving reasonable accuracy compared to fine-scale results. The method's robust performance suggests a strong potential for the practical application to large-scale SAGD operations.

© 2025 The Author(s). Published by The Geological Society of London for GSL and EAGE. All rights, including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved. For permissions: https://www.lyellcollection.org/publishing-hub/permissions-policy. Publishing disclaimer: https://www.lyellcollection.org/publishing-hub/publishing-ethics

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2025-11-19
2026-01-23

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References

  1. Albahlani, A.M. and Babadagli, T.2008. A critical review of the status of SAGD: Where are we and what is next?Paper SPE-113283-MS, presented at theSPE Western Regional and Pacific Section AAPG Joint Meeting, March 29–April 2, 2008, Bakersfield, California, USA, doi: 10.2118/113283-MS10.2118/113283‑MS
     https://doi.org/10.2118/113283-MS [Google Scholar]
  2. Al-Bahlani, A.-M. and Babadagli, T.2009. SAGD laboratory experimental and numerical simulation studies: A review of current status and future issues. Journal of Petroleum Science and Engineering, 68, 135–150, doi: 10.1016/j.petrol.2009.06.01110.1016/j.petrol.2009.06.011
     https://doi.org/10.1016/j.petrol.2009.06.011 [Google Scholar]
  3. Begg, S.H., Carter, R.R. and Dranfield, P.1989. Assigning effective values to simulator gridblock parameters for heterogeneous reservoirs. SPE Reservoir Engineering, 4, 455–463, doi: 10.2118/16754-pa10.2118/16754‑pa
     https://doi.org/10.2118/16754-pa [Google Scholar]
  4. Cante Soler, C.A., Malagueta, D.C. and Guerrero Martin, C.A.2023. Feasibility of implementation of solar thermal energy in steam-assisted gravity drainage (SAGD) in extra-heavy oil field in Colombia. Geoenergy Science and Engineering, 222, doi: 10.1016/j.geoen.2023.21146310.1016/j.geoen.2023.211463
     https://doi.org/10.1016/j.geoen.2023.211463 [Google Scholar]
  5. Card, C.C., Chakrabarty, N. and Gates, I.D.2006. Automated global optimization of commercial SAGD operations. Paper PETSOC-2006-157-EA, presented at theCanadian International Petroleum Conference, June 13–15, 2006, Calgary, Alberta, Canada, doi: 10.2118/2006-157-EA10.2118/2006‑157‑EA
     https://doi.org/10.2118/2006-157-EA [Google Scholar]
  6. Centeno, G., Sánchez-Reyna, G., Ancheyta, J., Muñoz, J.A.D. and Cardona, N.2011. Testing various mixing rules for calculation of viscosity of petroleum blends. Fuel, 90, 3561–3570, doi: https://doi.org/10.1016/j.fuel.2011.02.028https://doi.org/10.1016/j.fuel.2011.02.028
     https://doi.org/https://doi.org/10.1016/j.fuel.2011.02.028 [Google Scholar]
  7. Chen, Y.2005. Upscaling and Subgrid Modeling of Flow and Transport in Heterogeneous Reservoirs. PhD thesis, Stanford University, Stanford, California, USA.
     [Google Scholar]
  8. Cui, G., Liu, T., Xie, J., Rong, G. and Yang, L.2022. A review of SAGD technology development and its possible application potential on thin-layer super-heavy oil reservoirs. Geoscience Frontiers, 13, doi: 10.1016/j.gsf.2022.10138210.1016/j.gsf.2022.101382
     https://doi.org/10.1016/j.gsf.2022.101382 [Google Scholar]
  9. Deng, X.2005. Recovery performance and economics of steam/propane hybrid process. Paper SPE-97760-MS, presented at theSPE International Thermal Operations and Heavy Oil Symposium, November 1–3, 2005, Calgary, Alberta, Canada, doi: 10.2118/97760-MS10.2118/97760‑MS
     https://doi.org/10.2118/97760-MS [Google Scholar]
  10. Deutsch, C.1989. Calculating effective absolute permeability in sandstone/shale sequences. SPE Formation Evaluation, 4, 343–348, doi: 10.2118/17264-pa10.2118/17264‑pa
     https://doi.org/10.2118/17264-pa [Google Scholar]
  11. Durlofsky, L.J.2005. Upscaling and gridding of fine scale geological models for flow simulation. Paper presented at the8th International Forum on Reservoir Simulation, June 20–24, 2005, Iles Borromees, Stresa, Italy.
     [Google Scholar]
  12. Farmer, C.L.2002. Upscaling: a review. International Journal for Numerical Methods in Fluids, 40, 63–78, doi: 10.1002/fld.26710.1002/fld.267
     https://doi.org/10.1002/fld.267 [Google Scholar]
  13. Hampton, T., Kumar, D., Azom, P. and Srinivasan, S.2013. Analysis of impact of thermal and permeability heterogeneity on SAGD performance using a semi-analytical approach. Paper SPE-165565-MS, presented at theSPE Heavy Oil Conference-Canada, June 11–13, 2013, Calgary, Alberta, Canada, doi: 10.2118/165565-MS10.2118/165565‑MS
     https://doi.org/10.2118/165565-MS [Google Scholar]
  14. Hashemi-Kiasari, H., Hemmati-Sarapardeh, A., Mighani, S., Mohammadi, A.H. and Sedaee-Sola, B.2014. Effect of operational parameters on SAGD performance in a dip heterogeneous fractured reservoir. Fuel, 122, 82–93, doi: 10.1016/j.fuel.2013.12.05710.1016/j.fuel.2013.12.057
     https://doi.org/10.1016/j.fuel.2013.12.057 [Google Scholar]
  15. Hazra, K.G., Lee, K., Economides, C. and Moridis, G.2013. Comparison of heating methods for in-situ oil shale extraction. In: IOR 2013 – 17th European Symposium on Improved Oil Recovery, Saint Petersburg, Russia. European Association of Geoscientists & Engineers (EAGE), Houten, The Netherlands, doi: 10.3997/2214-4609.2014263110.3997/2214‑4609.20142631
     https://doi.org/10.3997/2214-4609.20142631 [Google Scholar]
  16. Heidari, M.2014. Equation of State Based Thermal Compositional Reservoir Simulator for Hybrid Solvent/Thermal Processes. PhD thesis, University of Calgary, http://hdl.handle.net/11023/1439
     [Google Scholar]
  17. Holden, L. and Nielsen, B.F.2000a. Global upscaling of permeability in heterogeneous reservoirs; the output least squares (OLS) method. Transport in Porous Media, 40, 115–143, doi: 10.1023/A:100665751575310.1023/A:1006657515753
     https://doi.org/10.1023/A:1006657515753 [Google Scholar]
  18. Holden, L. and Nielsen, B.2000b. Upscaling of permeability using global norms. Paper presented atECMOR VII – 7th European Conference on the Mathematics of Oil Recovery Conference, September 5–8, 2000, Baveno, Lago Maggiore, Italy, https://www.academia.edu/33948637/Upscaling_of_permeability_using_global_norms
     [Google Scholar]
  19. Huang, J. and Babadagli, T.2025. Pore-scale dynamics of steam assisted gravity drainage with hydrocarbon solvents (HC-SAGD) for technically and environmentally efficient heavy oil recovery. Journal of Cleaner Production, 491, doi: 10.1016/j.jclepro.2025.14483310.1016/j.jclepro.2025.144833
     https://doi.org/10.1016/j.jclepro.2025.144833 [Google Scholar]
  20. Ivory, J., Zheng, R., Nasr, T., Deng, X., Beaulieu, G. and Heck, G.2008. Investigation of low pressure ES-SAGD. Paper SPE-117759-MS, presented at theInternational Thermal Operations and Heavy Oil Symposium, October 20–23, 2008, Calgary, Alberta, Canada, doi: 10.2118/117759-MS10.2118/117759‑MS
     https://doi.org/10.2118/117759-MS [Google Scholar]
  21. Jiang, Q., You, H., Pan, J., Wang, Z., Gai, P.Y., Gates, I. and Liu, J.L.2020. Preliminary discussion on current status and development direction of heavy oil recovery technologies. Special Oil & Gas Reservoirs, 27, 30–39, doi: 10.3969/j.issn.1006-6535.2020.06.00410.3969/j.issn.1006‑6535.2020.06.004
     https://doi.org/10.3969/j.issn.1006-6535.2020.06.004 [Google Scholar]
  22. Kelkar, S., Pawar, R. and Hoda, N.2011. Numerical simulation of coupled thermal–hydrological–mechanical–chemical processes during in situ conversion and production of oil shale. Paper presented at the31st Oil Shale Symposium, October 17–19, 2011, Golden, Colorado, USA.
     [Google Scholar]
  23. Kumar, A. and Hassanzadeh, H.2021. Impact of shale barriers on performance of SAGD and ES-SAGD – a review. Fuel, 289, doi: 10.1016/j.fuel.2020.11985010.1016/j.fuel.2020.119850
     https://doi.org/10.1016/j.fuel.2020.119850 [Google Scholar]
  24. Kumar, D.2014. Modeling Steam Assisted Gravity Drainage in Heterogeneous Reservoirs Using Different Upscaling Techniques. MSc thesis, University of Texas atAustin, Austin, Texas, USA.
     [Google Scholar]
  25. Kumar, D., Murugesu, M. and Srinivasan, S.2014. Modeling effect of permeability heterogeneities on SAGD performance using improved upscaling schemes. Paper SPE-170115-MS, presented at theSPE Heavy Oil Conference-Canada, June 10–12, 2014, Calgary, Alberta, Canada, doi: 10.2118/170115-MS10.2118/170115‑MS
     https://doi.org/10.2118/170115-MS [Google Scholar]
  26. Lake, L., Johns, R.T., Rossen, W.R. and Pope, G.A.2014. Fundamentals of Enhanced Oil Recovery. Society of Petroleum Engineers, Richardson, TX.
     [Google Scholar]
  27. Lee, J. and Babadagli, T.2021. Mitigating greenhouse gas intensity through new generation techniques during heavy oil recovery. Journal of Cleaner Production, 286, doi: 10.1016/j.jclepro.2020.12498010.1016/j.jclepro.2020.124980
     https://doi.org/10.1016/j.jclepro.2020.124980 [Google Scholar]
  28. Li, H.2014. Compositional Upscaling for Individual Models and Ensembles of Realizations. PhD thesis, Stanford University, Stanford, California, USA.
     [Google Scholar]
  29. Li, H., Chen, Y., Rojas, D. and Kumar, M.2014. Development and application of near-well multiphase upscaling for forecasting of heavy oil primary production. Journal of Petroleum Science and Engineering, 113, 81–96, doi: 10.1016/j.petrol.2014.01.00210.1016/j.petrol.2014.01.002
     https://doi.org/10.1016/j.petrol.2014.01.002 [Google Scholar]
  30. Li, H., Vink, J.C. and Alpak, F.O.2016. A dual-grid method for the upscaling of solid-based thermal reactive flow, with application to the in-situ conversion process. SPE Journal, 21, 2097–2111, doi: 10.2118/173248-pa10.2118/173248‑pa
     https://doi.org/10.2118/173248-pa [Google Scholar]
  31. Liu, Y., Wu, L., Guo, J., He, S. and Wu, Y.2024. Model for fracture conductivity considering particle size redistribution caused by proppant crushing. Geoenergy Science and Engineering, 240, doi: 10.1016/j.geoen.2024.21308110.1016/j.geoen.2024.213081
     https://doi.org/10.1016/j.geoen.2024.213081 [Google Scholar]
  32. Liu, Y., Wu, L., Guo, J., Wu, Y., Yan, D. and Zhang, T.2025. Whole process simulation for efficient proppant placement technology. Geoenergy Science and Engineering, 254, doi: 10.1016/j.geoen.2025.21404510.1016/j.geoen.2025.214045
     https://doi.org/10.1016/j.geoen.2025.214045 [Google Scholar]
  33. Luo, H.2013. The Reservoir Forming Main Control Factors and Distribution Law of the Upper Cretaceous Slope Migration Type Oil Sands in Songliao Basin. PhD thesis, Jilin University, Changchun, Jilin, China [in Chinese].
     [Google Scholar]
  34. Meng, S., Fu, Q., Tao, J., Liang, L. and Xu, J.2024. Predicting CO2-EOR and storage in low-permeability reservoirs with deep learning-based surrogate flow models. Geoenergy Science and Engineering, 233, doi: 10.1016/j.geoen.2023.21246710.1016/j.geoen.2023.212467
     https://doi.org/10.1016/j.geoen.2023.212467 [Google Scholar]
  35. Murugesu, M.2015. Improved Upscaling Scheme for Steam Assisted Gravity Drainage (SAGD) and Semi-Analytical Modeling of the SAGD Rising Phase. MSc thesis, University ofTexas., Austin, Texas, USA.
     [Google Scholar]
  36. Nakashima, T. and Durlofsky, L.J.2010. Accurate representation of near-well effects in coarse-scale models of primary oil production. Transport in Porous Media, 83, 741–770, doi: 10.1007/s11242-009-9479-x10.1007/s11242‑009‑9479‑x
     https://doi.org/10.1007/s11242-009-9479-x [Google Scholar]
  37. Nakashima, T., Li, H. and Durlofsky, L.J.2012. Near-well upscaling for three-phase flows. Computational Geosciences, 16, 55–73, doi: 10.1007/s10596-011-9252-410.1007/s10596‑011‑9252‑4
     https://doi.org/10.1007/s10596-011-9252-4 [Google Scholar]
  38. Perez-Perez, A., Mujica Chacín, M., Bogdanov, I., Brisset, A. and Garnier, O.2019. Simulations of in-situ upgrading process: interpretation of laboratory experiments and study of field-scale test. SPE Journal, 24, 2711–2730, doi: 10.2118/190695-PA10.2118/190695‑PA
     https://doi.org/10.2118/190695-PA [Google Scholar]
  39. Pratama, R.A. and Babadagli, T.2023. What did we learn from SAGD applications in three decades, and what is next?Paper SPE-212970-MS, presented at theSPE Western Regional Meeting, May 22–25, 2023, Anchorage, Alaska, USA, doi: 10.2118/212970-MS10.2118/212970‑MS
     https://doi.org/10.2118/212970-MS [Google Scholar]
  40. Salazar, M.O. and Villa, J.R.2007. Permeability upscaling techniques for reservoir simulation. Paper SPE-106679-MS, presented at theLatin American & Caribbean Petroleum Engineering Conference, April 15–18, 2007, Buenos Aires, Argentina, doi: 10.2118/106679-MS10.2118/106679‑MS
     https://doi.org/10.2118/106679-MS [Google Scholar]
  41. Shen, C.2009. Reservoir simulation study of an in-situ conversion pilot of Green-River oil shale. Paper SPE-123142-MS, presented at theSPE Rocky Mountain Petroleum Technology Conference, April 14–16, 2009, Denver, Colorado, USA, doi: 10.2118/123142-MS10.2118/123142‑MS
     https://doi.org/10.2118/123142-MS [Google Scholar]
  42. Sun, H., Liu, H., Wang, H., Shu, Q., Wu, G. and Yang, Y.2022. Development technology and direction of thermal recovery of heavy oil in China. Acta Petrolei Sinica, 43, 1664–1674 [in Chinese], https://www.syxb-cps.com.cn/EN/10.7623/syxb202211013
     [Google Scholar]
  43. Sun, X., Xu, B., Qian, G. and Li, B.2021. The application of geomechanical SAGD dilation startup in a Xinjiang oil field heavy-oil reservoir. Journal of Petroleum Science and Engineering, 196, doi: 10.1016/j.petrol.2020.10767010.1016/j.petrol.2020.107670
     https://doi.org/10.1016/j.petrol.2020.107670 [Google Scholar]
  44. Takbiri-Borujeni, A., Mohammadnia, V., Mansouri Boroujeni, M., Nourozieh, H. and Ghahfarokhi, P.K.2019. Upscaling the steam-assisted-gravity-drainage model for heterogeneous reservoirs. SPE Journal, 24, 1681–1699, doi: 10.2118/195587-pa10.2118/195587‑pa
     https://doi.org/10.2118/195587-pa [Google Scholar]
  45. Tang, L.2017. Analysis of the current situation and development trend of heavy oil thermal recovery technology. Chemical Engineering and Equipment, 9, 282–283, doi: 10.19566/j.cnki.cn35-1285/tq.2017.09.09610.19566/j.cnki.cn35‑1285/tq.2017.09.096
     https://doi.org/10.19566/j.cnki.cn35-1285/tq.2017.09.096 [Google Scholar]
  46. Wang, Y., Liu, H. and Zhou, Y.2021. Development of a deep learning-based model for the entire production process of steam-assisted gravity drainage (SAGD). Fuel, 287, doi: 10.1016/j.fuel.2020.11956510.1016/j.fuel.2020.119565
     https://doi.org/10.1016/j.fuel.2020.119565 [Google Scholar]
  47. White, C.D. and Horne, R.N.1987. Computing absolute transmissibility in the presence of fine-scale heterogeneity. Paper SPE-16011-MS, presented at theSPE Symposium on Reservoir Simulation, February 1–4, 1987, San Antonio, Texas, USA, doi: 10.2118/16011-MS10.2118/16011‑MS
     https://doi.org/10.2118/16011-MS [Google Scholar]
  48. Wu, L., Liu, Y., Guo, J., Wu, Y., He, S. and Yan, D.2025. CFD-DEM simulation of composite plugging using novel sheet fibers in rough fractures. Energy, 319, doi: 10.1016/j.energy.2025.13489510.1016/j.energy.2025.134895
     https://doi.org/10.1016/j.energy.2025.134895 [Google Scholar]
  49. Xi, C., Yang, Z. et al.2019. Three typical SAGD horizontal producer temperature modes and enhanced measures in heterogeneous super heavy oil reservoir – A case study in FC Project of Xinjiang Oilfield. Paper SPE-196762-MS, presented at theSPE Russian Petroleum Technology Conference, October 22–24, 2019, Moscow, Russia, doi: 10.2118/196762-MS10.2118/196762‑MS
     https://doi.org/10.2118/196762-MS [Google Scholar]
/content/journals/10.1144/petgeo2025-033
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A novel upscaling method for steam-assisted gravity drainage simulations based on a viscosity–temperature model
Petroleum Geoscience 31, petgeo2025-033 (2025); https://doi.org/10.1144/petgeo2025-033
/content/journals/10.1144/petgeo2025-033
/content/journals/10.1144/petgeo2025-033
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