Skip to main content

Advertisement

SpringerLink
Log in

A Statistical Upscaling Workflow for Warm Solvent Injection Processes for Heterogeneous Heavy Oil Reservoirs

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Hybrid solvent- and thermal-based methods offer an important potential for reducing the environmental impact and enhancing recoveries in heavy oil production processes. Time-consuming compositional simulations of fine-scale models challenge the optimization of operational conditions. This work developed a flow-based upscaling workflow for simulating the warm solvent injection process in heterogeneous reservoirs. This process takes into account the pattern of spatial heterogeneity at different scales. A set of coarse-scale models was considered to be constructed to capture the subscale variability due to coarsening. In this study, scale-up of several static (porosity, permeability) and dynamic or transport parameters (dispersivities) was considered. In order to depict the spatial heterogeneity below the modeling scale, several realizations of sub-scale models of the modeling-scale cell size were created. The difference in the flow simulation responses of a heterogeneous realization and an equivalent average model was minimized to estimate effective longitudinal and transverse dispersivities. Results from all realizations were aggregated to construct the probability distributions of effective dispersivities. Field data of typical Athabasca bitumen reservoirs were used to generate a set of synthetic fine-scale models to test the workflow. The method is flexible, as no explicit assumption regarding the multivariate distribution of the heterogeneity is required. This paper extends our previous work, which focuses on solute transport in single-phase flow, to adopting this statistical scale-up framework for a complex hybrid solvent-thermal process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18

Similar content being viewed by others

Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code Availability

The workflow is developed in a custom code in MATLAB®. Flow simulations are performed using a commercial software package (STARS by the Computer Modelling Group(CMG)). The sequential simulation code is part of an open-source algorithm provided in GitHub by Nussbaumer (2019).

Notes

  1. 1 mD = 0.001 darcy; 1 darcy = 0.9869233 µm2.

References

  • Adale, O. S., Hamdy, M., Mahmoud, M., Al Shehri, D. A., Mokheimer, E., & Al-Nakhli, A. (2020). Experimental and numerical analysis of using thermochemical injection for preheating to improve in-situ combustion of bitumen. Fuel, 275, 117894.

    Google Scholar 

  • Ahmadi, M. A., Zendehboudi, S., Bahadori, A., James, L., Lohi, A., Elkamel, A., & Chatzis, I. (2014). Recovery rate of vapor extraction in heavy oil reservoirs- experimental, statistical, and modeling studies. Industrial & Engineering Chemistry Research, 53(41), 16091–16106.

    Google Scholar 

  • Ahmed, T. (2010). Reservoir engineering handbook. Elsevier Inc.

    Google Scholar 

  • Ardali, M., Barrufet, M. A., Mamora, D. D., & Qiu, F. (2012). A critical review of hybrid steam-solvent processes to recover heavy oil. SPE. https://doi.org/10.2118/159257-MS

    Article  Google Scholar 

  • Arya, A., Hewett, T. A., Larson, R. G., & Lake, L. W. (1988). Dispersion and reservoir heterogeneity. SPE Reservoir Engineering, 3(1), 139–148.

    Google Scholar 

  • Barker, J. W., & Fayers, F. J. (1994). Transport coefficients for compositional simulation with coarse grid in heterogeneous media. SPE Advanced Technology Series, 2(2), 103–112.

    Google Scholar 

  • Bear, J. (1979). Hydraulics of groundwater. New York: McGraw-Hill.

    Google Scholar 

  • Bear, J., & Buchlin, J. M. (1991). Modelling and applications of transport phenomena in porous media. Theory and Applications of Transport in Porous Media, 5(153–157), 381.

    Google Scholar 

  • Berkowitz, B., & Scher, H. (2010). Anomalous transport in correlated velocity fields. Physical Review, 81(1), 011128.

    Google Scholar 

  • Berkowitz, B., Scher, H., & Silliman, S. E. (2000). Anomalous transport in laboratory-scale, heterogeneous porous media. Water Resource Research, 36(1), 149–158.

    Google Scholar 

  • Binning, P., & Celia, M. A. (2002). A forward particle tracking Eulerian-Lagrangian localized adjoint method for solution of the contaminant transport equation in three dimensions. Advances in Water Resources, 25(2), 147–157.

    Google Scholar 

  • Butler, R., McNab, G., & Lo, H. (1981). Theoretical studies on the gravity drainage of heavy oil during in-situ steam heating. Canadian Journal of Chemical Engineering, 59(4), 455–460.

    Google Scholar 

  • Butler, R. M., & Mokrys, I. J. (1991). A new process (VAPEX) for recovering heavy oils using hot water and hydrocarbon vapor. Journal of Canadian Petroleum Technology, 30, 97–106.

    Google Scholar 

  • Computer Modeling Group. (2019). STARS User Guide.

  • Cuthiell, D., McCarthy, C., Kissel, G. et al. (2006). The role of Capillarity in VAPEX. In: Presented at the Canadian International Petroleum Conference, Calgary, 13–15 June, PETSOC-2006–073. https://doi.org/10.2118/2006-073.

  • Das, S. K., & Butler, R. M. (1996a). Countercurrent extraction of heavy oil and bitumen. In: International Conference on Horizontal Well Technology, 18–20 November, Calgary, Alberta, Canada, SPE- 37094, 501–510.

  • Das, S. K., & Butler, R. M. (1996b). Diffusion coefficients of propane and butane in peace river bitument. Canadian Journal of Chemical Engineering, 74(6), 985–992.

    Google Scholar 

  • Deng, X., Huang, H., Zhao, L., Law, D.H.-S., & Nasr, T. N. (2008). Simulating the ES-SAGD process with solvent mixture in Athabasca reservoirs. Journal of Canadian Petroleum Technology, 49(1), 38–46.

    Google Scholar 

  • Deutsch, C. V. (2004). A statistical resampling program for correlated data: Spatial\_Bootstrap. Technical report, CCG Annual Report 6–401, Centre for Computational Geostatistics, University of Alberta. Retrieved from 1 Aug 2020 http://www.ccgalberta.com/resources/reports/

  • Ewing, R. P. (2005). Transport processes. Encyclopedia of Soils in the Environment (pp. 77–485). Elsevier Ltd.

    Google Scholar 

  • Fanchi, J. R. (1983a). Multidimensional numerical dispersion. Society of Petroleum Engineers. https://doi.org/10.2118/9018-PA

    Article  Google Scholar 

  • Fanchi, J. R. (1983b). Multidimensional numerical dispersion. SPE Journal, 23(1), 143–151.

    Google Scholar 

  • Fleurant, C., & Van Der Lee, J. (2001). A stochastic model of transport in three-dimensional porous media. Mathematical Geology, 33(4), 449–474.

    Google Scholar 

  • Garmeh, G., & Johns, R. T. (2010). Upscaling of miscible floods in heterogeneous reservoirs considering reservoir mixing. SPE. https://doi.org/10.2118/124000-MS

    Article  Google Scholar 

  • Gates, I. D., & Larter, S. R. (2014). Energy efficiency and emissions intensity of SAGD. Fuel, 115, 706–713.

    Google Scholar 

  • Gelhar, L. W., Welty, C., & Rehfeldt, K. R. (1992). A critical review of data on field-scale dispersion in aquifers. Water Resources Research, 28(7), 1955–1974.

    Google Scholar 

  • Ghanbamezhad, R. M. (2011). Numerical dispersion impact on local mixing in heterogeneous reservoirs. In: SPE Eastern Regional Meeting held in Columbus, Ohio, USA 17019. SPE 149420. https://doi.org/10.2118/149420-MS

  • Ghanbari, E., Mighani, S., Shaabani, E., & Alipour, R. (2011). Improving SAGD performance combining with CSS. In: International Petroleum Technology Conference. doi: https://doi.org/10.2523/IPTC-14273-MS

  • Government of Alberta. (2017). Oil sands fact sheet. 1–2. Retrieved from www.aer.ca.

  • Hunyinbo, S., Ma, Z., & Leung, J. Y. (2021). Incorporating phase behavior constraints in multi-objective optimization of warm vaporized solvent injection process. Journal of Petroleum Science and Engineering, 205, 108949.

    Google Scholar 

  • Irani, M., & Ghannadi, S. (2013). Understanding the heat-transfer mechanism in the steam-assisted gravity-drainage (SAGD) process and comparing the conduction and convection flux in bitumen reservoirs. SPE Journal. https://doi.org/10.2118/163079-PA

    Article  Google Scholar 

  • Jiang, T., Jia, X., Zeng, F., Gu, Y. (2013). A novel solvent injection technique for enhanced heavy oil recovery: Cyclic production with continuous solvent injection. In: Paper presented at the SPE Heavy Oil Conference-Canada, Calgary, Alberta, Canada, June 2013. SPE 165455.

  • John, A.K. (2008). Dispersion in large scale permeable media. Dissertation, University of Texas at Austin.

  • Lake, L. W. (1989). Enhanced oil recovery. Prentice Hall.

    Google Scholar 

  • Lake, L. W., & Srinivasan, S. (2004). Statistical scale-up of reservoir properties: Concepts and applications. Journal of Petroleum Science and Engineering, 44(1), 27–39.

    Google Scholar 

  • Lantz, R. B. (1971). Quantitative evaluation of numerical diffusion (Truncation Error). SPE Journal. https://doi.org/10.2118/2811-PA

    Article  Google Scholar 

  • Leung, J. Y., & Srinivasan, S. (2011). Analysis of uncertainty introduced by scale-up of reservoir attributes and flow response in heterogeneous reservoirs. SPE Journal, 16(3), 713–724.

    Google Scholar 

  • Leung, J. Y., & Srinivasan, S. (2016). Effects of reservoir heterogeneity on scaling of effective mass transfer coefficient for solute transport. Journal of Contaminant Hydrology, 192, 181–193.

    Google Scholar 

  • Leung, J. Y., & Srinivasan, S. (2012). Scale-up of mass transfer and recovery performance in heterogeneous reservoirs. Journal of Petroleum Science and Engineering, 86–87, 71–86.

    Google Scholar 

  • Li, H., & Durlofsky, L. J. (2016). Upscaling for compositional reservoir simulation. SPE. https://doi.org/10.2118/173212-PA

    Article  Google Scholar 

  • Li, W., & Mamora, D. D. (2010). Phase behavior of steam with solvent coinjection under steam assisted gravity drainage (SAGD) process. SPE. https://doi.org/10.2118/130807-MS

    Article  Google Scholar 

  • Lichtner, P. C., & Kang, Q. (2007). Upscaling pore-scale reactive transport equations using a multiscale continuum formulation. Water Resource Research, 43(12), 1–19.

    Google Scholar 

  • Ma, Z., & Leung, J. Y. (2020a). Design of warm solvent injection processes for heterogeneous heavy oil reservoirs: a hybrid workflow of multi-objective optimization and proxy models. Journal of Petroleum Science and Engineering, 191, 107186.

    Google Scholar 

  • Ma, Z., & Leung, J. Y. (2020b). Knowledge-based heterogeneity characterization framework for 3D steam-assisted gravity drainage reservoirs. Knowledge-Based Systems, 192, 10537.

    Google Scholar 

  • Ma, Z., & Leung, J. Y. (2020c). Integration of deep learning and data analytics for SAGD temperature and production analysis. Computational Geosciences, 24, 1239–1255.

    Google Scholar 

  • Ma, Z., Leung, J. Y., Zanon, S., & Dzurman, P. (2015). Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis. Expert Systems with Applications, 42(21), 7326–7343.

    Google Scholar 

  • Mendoza, A. M., Babak, P., & Kanzas, A. (2018). Estimation of diffusion coefficients in liquid solvent bitumen systems. Energy & Fuels, 32(11), 11143–11152.

    Google Scholar 

  • Moghadam, S., Nobakht, M., & Gu, Y. (2009). Theoretical and physical modeling of a solvent vapour extraction (VAPEX) process for heavy oil recovery. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2008.12.029

    Article  Google Scholar 

  • Mokrys, I. J., & Butler, R. M. (1993). The rise of interfering solvent chambers: solvent analog model of steam assisted gravity drainage. Journal of Canadian Petroleum Technology. https://doi.org/10.2118/93-03-02

    Article  Google Scholar 

  • Nenniger, J., & Nenniger, E. (2001). Method and apparatus for stimulating heavy oil production. Canadian Patent CA2351148A1.

  • Nenniger, J. E., & Dunn, S. G. (2008). How fast is solvent based gravity drainage? Petroleum Society of Canada. https://doi.org/10.2118/2008-139

    Article  Google Scholar 

  • NSolv. (2016). CCEMC Project ID: H110031 - BEST Pilot Project Non-Confidential Final Report. Alberta Innovates – Energy and Environment Solutions, p.37. Retrieved from 16 Dec 2019 http://eralberta.ca/wp-content/uploads/2017/05/H110031-Nsolv-BEST-Field-Pilot-Final-Report-Public.pdf

  • Nussbaumer, R. (2019). Sequential Gaussian Simulation (Version 1.2). <Source code> Retrieved from 1 Nov 2019 https://rafnuss-phd.github.io/SGS/

  • Oz, B., & Deutsch, C. V. (2002). Size scaling of cross correlation between multiple variables. Natural Resources Research, 11, 1–18.

    Google Scholar 

  • Perkins, T. K., & Johnston, O. C. (1963). A review of diffusion and dispersion in porous media. SPE Journal, 3(01), 70–84. https://doi.org/10.2118/480-PA

    Article  Google Scholar 

  • Pickens, J. F., & Grisak, G. E. (1981). Scale-dependent dispersion in a stratified granular aquifer. Water Resources Research, 17(4), 1191–1211.

    Google Scholar 

  • Pyrcz, M. J., & Deutsch, C. V. (2014). Geostatistical Reservoir Modeling (2nd ed., p. 448). Oxford University Press.

    Google Scholar 

  • Rahnema, M., Rahnema, H., Mcmillan, M. D., Edrisi, A. R., & Rahnema, H. (2016). Numerical study of single well vapor extraction process. Journal of Petroleum Engineering. https://doi.org/10.1155/2016/8925190

    Article  Google Scholar 

  • Rezaei, N., & Chatzis, I. (2007). Incorporation of heat in the VAPEX process: Warm VAPEX. Petroleum Society of Canada. https://doi.org/10.2118/2007-133-EA

    Article  Google Scholar 

  • Rezvandehy, M., Leung, J. Y., Ren, W., Hollands, B., & Pan, G. (2019). An improved workflow for permeability estimation from image logs with uncertainty quantification. Natural Resources Research, 28(3), 777–811.

    Google Scholar 

  • Saaltink, M. W., Ayora, C., & Carrera, J. (1998). A mathematical formulation for reactive transport that eliminates mineral concentration. Water Resources Research, 34(7), 1649–1656.

    Google Scholar 

  • Salehi, A., Voskov, D. V., & Tchelepi, H. A. (2013). Thermodynamically consistent transport coefficients for upscaling of compositional processes. Society of Petroleum Engineers. https://doi.org/10.2118/163576-MS

    Article  Google Scholar 

  • Sammon, P. H. (2003). Dynamic grid refinement and amalgamation for compositional simulation. Society of Petroleum Engineers. Presented in Houston, Texas, U.S.A., 3–5 February 2003. doi:https://doi.org/10.2118/79683-MS

  • Shi, J., & Leung, J. Y. (2014b). Semi-analytical proxy for vapex process modeling in heterogeneous reservoirs. ASME Journal of Energy Resources Technology, 136(3), 032904.

    Google Scholar 

  • Shi, J., & Leung, J. Y. (2014a). Physics-based proxy modelling of solvent transport in VAPEX process. Canadian Journal of Chemical Engineering, 92(8), 1467–1480.

    Google Scholar 

  • Shi, J., Vishal, V., & Leung, J. Y. (2014). Uncertainty assessment of vapex performance in heterogeneous reservoirs using a semi-analytical proxy model. Journal of Petroleum Science and Engineering, 122, 290–303.

    Google Scholar 

  • Suncor MacKay River Project. (2019). AER Performance Presentation Reporting Period: September 1, 2018 to August 31, Retrieved from https://www.aer.ca/documents/oilsands/insitu-presentations/2019AthabascaSuncorMacKaySAGD8668-Presentation.pdf

  • Takbiri-Borujeni, A., Mohammadnia, V., Mansouri-Boroujeni, M., Nourozieh, H., & Kavousi Ghahfarokhi, P. (2019). Upscaling the steam-assisted-gravity-drainage model for heterogeneous reservoirs. Society of Petroleum Engineers. https://doi.org/10.2118/195587-PA

    Article  Google Scholar 

  • Taylor, G. I. (1953). Dispersion of solute matter in solvent flowing through a tube. Proceedings of the Royal Society of London, Series a: Mathematical and Physical Sciences, 219, 186–203.

    Google Scholar 

  • Vishal, V., & Leung, J. Y. (2015). Modeling impacts of subscale heterogeneities on dispersive solute transport in subsurface systems. Journal of Contaminant Hydrology, 182, 63–77.

    Google Scholar 

  • Vishal, V., & Leung, J. Y. (2017a). Statistical framework for scale-up of dispersivity in multiscale heterogeneous media. Environment and Earth Science, 76, 624.

    Google Scholar 

  • Vishal, V., & Leung, J. Y. (2017b). A multi-scale particle-tracking framework for dispersive solute transport modeling. Computational Geosciences, 22(2), 485–503.

    Google Scholar 

  • Vishal, V., & Leung, J. Y. (2018a). A novel framework for integration of random-walk particle-tracking simulation in subsurface multi-phase flow modeling. Transport in Porous Media, 125(3), 609–631.

    Google Scholar 

  • Vishal, V., & Leung, J. Y. (2018b). Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling. Stochastic Environmental Research and Risk Assessment, 32, 2075–2091.

    Google Scholar 

  • Zendehboudi, S., Rajabzadeh, A. R., Bahadori, A., Chatzis, I., Dusseault, M. B., Elkamel, A., Lohi, A., & Fowler, M. (2014). Connectionist model to estimate performance of steam-assisted gravity drainage in fractured and unfractured petroleum reservoirs: Enhanced oil recovery implications. Industrial & Engineering Chemistry Research, 53(4), 1645–1662.

    Google Scholar 

  • Zhang, K., Zhou, X., Peng, X., & Zeng, F. (2019). A comparison study between N-Solv method and cyclic hot solvent injection (CHSI) method. Journal of Petroleum Science and Engineering, 173, 258–268.

    Google Scholar 

  • Zheng, J., Leung, J. Y., Sawatzky, R. P., & Alvarez, J. M. (2018). An AI-based workflow for estimating shale barrier configurations from SAGD production histories. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3365-9

    Article  Google Scholar 

Download references

Acknowledgments

The research is supported by the University of Alberta Future Energy Systems (FES), established by the Government of Canada's Canada First Research Excellence Fund (CFREF) with the project number T07-C02. We are thankful for providing the MATLAB® and STARS academic licenses to MathWorks™ and the Computer Modeling Group (CMG), respectively.

Author information

Authors and Affiliations

  1. School of Mining & Petroleum Engineering, University of Alberta, Edmonton, AB, Canada

    Elena Andriianova & Juliana Y. Leung

Authors
  1. Elena Andriianova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juliana Y. Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juliana Y. Leung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Andriianova, E., Leung, J.Y. A Statistical Upscaling Workflow for Warm Solvent Injection Processes for Heterogeneous Heavy Oil Reservoirs. Nat Resour Res 30, 4417–4437 (2021). https://doi.org/10.1007/s11053-021-09921-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-021-09921-6

Keywords

Search

Navigation