Characterizing the Effects of Lean Zones and Shale Distribution in Steam-Assisted-Gravity-Drainage Recovery Performance
- Cui Wang (University of Alberta) | Juliana Leung (University of Alberta)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2015
- Document Type
- Journal Paper
- 329 - 345
- 2015.Society of Petroleum Engineers
- shale barriers, reservoir heterogeneities, steam injection, heavy oil
- 4 in the last 30 days
- 529 since 2007
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Performance of steam-assisted gravity drainage (SAGD) is influenced significantly by the distributions of lean zones and shale barriers, which tend to impede the vertical growth and lateral spread of a steam chamber. Previous literature has partially addressed their effects on SAGD performance; however, a comprehensive and systematic investigation of the heterogeneous distribution (location, continuity, size, saturation, and proportions) of shale barriers and lean zones is still lacking. In this study, numerical simulations are used to model the SAGD process. Capillarity and relative permeability effects, which were ignored in many previous simulation studies, are incorporated to model bypassed oil. Numerous ranking schemes are formulated to analyze various aspects of SAGD performance. A detailed sensitivity analysis is performed by varying the location, continuity, size, proportions, and saturation of these heterogeneous features. Lean zones and shale lenses (imbedded in a region of degraded rock properties) with different sizes and degrees of continuity are placed in areas above the injector, below the producer, or in between the well pair. It is noted that among numerous parameters that influence the ultimate recovery, remaining bypassed oil, chamber advancement, and heat loss, continuity and position of these features in relation to the well pair play a particularly crucial role. Neural network modeling is subsequently used for constructing data-driven models to identify and propose a set of input variables for correlating relevant parameters or measures, which are descriptive of the heterogeneity and properties of the shale barriers and lean zones, to recovery and ranking results. This work provides a guideline for assessing the impacts of reservoir and saturation heterogeneities on SAGD performance. A set of input variables and parameters that have significant impacts on the ensuing recovery response is identified. One can define readily the proposed set of variables from well logs and apply immediately in data-driven models with field data and scaleup analysis of experimental models to assist field-operation design and evaluation. One can also extend the approach presented in this paper to analyze other solvent-assisted SAGD processes.
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Albahlani, A. M. and Babadagli, T. 2008. A Critical Review of the Status of SAGD: Where Are We and What Is Next? Presented at the SPE Western Regional and Pacific Section AAPG Joint Meeting, Bakersfield, California, USA, 29 March–4 April. SPE-113283-MS. http://dx.doi.org/10.2118/113283-MS.
Al-Fattah, S. M. and Startzman, R. A. 2001. Predicting Natural-Gas Production Using Artificial Neural Network. Paper presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, Texas, USA, 2–3 April. SPE-68593-MS. http://dx.doi.org/10.2118/68593-MS.
Amirian, E., Leung, J. Y., Zanon, S. et al. 2013. Data-Driven Modeling Approach for Recovery Performance Prediction in SAGD Operations. Presented at the SPE Heavy Oil Conference, Calgary, Alberta, Canada, 11–13 June. SPE-165557-MS. http://dx.doi.org/10.2118/165557-MS.
Attanasi, E. D. and Meyer, R. F. 2007. Natural Bitumen and Extra-heavy Oil. In 2007 Survey of Energy Resources, ed. J. Trinnaman and A. Clarke, World Energy Council, 119–143.
Azad, A. and Chalaturnyk, R. J. 2010. A Mathematical Improvement to SAGD Using Geomechanical Modeling. J Can Pet Technol 49 (10): 53–64. SPE-141303-PA. http://dx.doi.org/10.2118/141303-PA.
Bishop, C. 1995. Neural Networks for Pattern Recognition. Oxford: Clarendon Press.
Botset, H. G. 1940. Flow of Gas-Liquid Mixtures Through Consolidated Sand. Trans., AIME. 136 (1): 91–105. SPE-940091-G-PA. http://dx.doi.org/10.2118/940091-G-PA.
Burton, R. C., Chin, L. Y., Davis, E. R. et al. 2005. North Slope Heavy-Oil Sand-Control Strategy: Detailed Case Study of Sand Production Predictions and Field Measurements for Alaskan Heavy-Oil Multilateral Field Developments. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, 9–12 October. SPE-97279-MS. http://dx.doi.org/10.2118/97279-MS.
Butler, R. M. and Mcnab, G. S. 1981. Theoretical Studies on the Gravity Drainage of Heavy Oil During In-Situ Steam Heating. Canadian J. Chemical Eng. 59 (4): 455–460. http://dx.doi.org/10.1002/cjce.5450590407.
Butler, R. M. 1985. A New Approach to the Modeling of Steam-Assisted Gravity Drainage. J Can Pet Technol 24 (3): 42–51. SPE-85-03-01-PA. http://dx.doi.org/10.2118/85-03-01-PA.
Chen, Q., Gerritsen, M. G., and Kovscek, A. R. 2008. Effects of Reservoir Heterogeneities on the Steam-Assisted Gravity-Drainage Process. SPE Res Eval & Eng 11 (5): 921–932. SPE-109873-PA. http://dx.doi.org/10.2118/109873-PA.
Chen, Q. 2009. Assessing and Improving Steam-Assisted Gravity Drainage: Reservoir Heterogeneities, Hydraulic Fractures, and Mobility Control. Dissertation, Stanford University (May 2009).
Computer Modelling Group. 2013. STARS: Advanced Processes & Thermal Reservoir Simulator User’s Guide (Version 2013). Calgary, Alberta, Canada: Computer Modeling Group Limited.
Dang, C. T. Q., Nguyen, N. T. B., Bae, W. et al. 2010. Investigation of SAGD Recovery Process in Complex Reservoir. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Brisbane, Queensland, Australia, 18–20 October. SPE-133849-MS. http://dx.doi.org/10.2118/133849-MS.
Farouq-Ali, S. M. 1983. Effect of Bottom Water and Gas Cap on Thermal Recovery. SPE Annual California Regional Meeting, Dallas, Texas, USA, 23–25 March. SPE-11732-MS. http://dx.doi.org/10.2118/11732-MS.
Francis, L. 2001. The Basics of Neural Networks Demystified. Contingencies 11 (12): 56–61.
Goetz, J. F., Prins, W. J., and Logar, J. F. 1977. Reservoir Delineation by Wireline Techniques. Proc., Indonesian Petroleum Association Sixth Annual Convention, 161–198.
Good, W. K., Claude, R., and Felty, B. D. 1997. Possible Effects of Gas Caps on SAGD Performance. ADOE/EUB Report.
Hampton, T., Kumar, D., Azom, P. et al. 2013. Analysis of Impact of Thermal and Permeability Heterogeneity on SAGD Performance Using a Semi-Analytical Approach. Presented at the SPE Heavy Oil Conference, Calgary, Alberta, Canada, 11–13 June. SPE-165565-MS. http://dx.doi.org/10.2118/165565-MS.
Hubbard, S. M., Smith, D. G., Nielsen, H. et al. 2011. Seismic Geomorphology and Sedimentology of a Tidally Influenced River Deposit, Lower Cretaceous Athabasca Oil Sands, Alberta, Canada. AAPG 95 (7): 1123–1145. http://dx.doi.org/10.1306/12131010111.
Ipek, G., Frauenfeld, T., and Yuan, J. Y. 2008. Numerical Study of Shale Issues in SAGD. Presented at the Canadian International Petroleum Conference, Calgary, Alberta, Canada, 17–19 June. SPE-2008-150-MS. http://dx.doi.org/10.2118/2008-150-MS.
Iwata, Y., Koseki, H., Janssens, M. L. et al. 2000. Comparison of Combustion Characteristics of Various Crude Oils. Presented at the International Association for Fire Safety Science. AOFST 4.
Kendall, G. H. 1977. Importance of Reservoir Description in Evaluating In Situ Recovery Methods for Cold Lake Heavy Oil–Part I: Reservoir Description. J Can Pet Technol 16 (1): 48–54. SPE-77-01-04-PA. http://dx.doi.org/10.2118/77-01-04-PA.
Law, D. H.-S., Nasr, T. N., and Goog, W. K. 2003. Field-Scale Numerical Simulation of SAGD Process With Top-Water Thief Zone. J Can Pet Technol 42 (8): 32–38. SPE-03-08-01-PA. http://dx.doi.org/10.2118/03-08-01-PA.
Lerat, O. O., Adjemian, F., Baroni, A. et al. 2010. Modeling of 4D Seismic Data for the Monitoring of Steam Chamber Growth During the SAGD Process. J Can Pet Technol 49 (6): 21–30. SPE-138401-PA. http://dx.doi.org/10.2118/138401-PA.
Leskiw, C. and Gates, L. D. 2012. Monitoring of SAGD Steam-Chamber Conformance by Using White-Noise-Reflection Processes. SPE J. 17 (4): 1246–1254. SPE-137750-PA. http://dx.doi.org/10.2118/137750-PA.
Ma, Z., Leung, J., Zanon, S. et al. 2014. Practical Implementation of Knowledge-Based Approaches for SAGD Production Analysis. Presented at the SPE Heavy Oil Conference Canada, Calgary, Canada, 10–12 June. SPE-170144-MS. http://dx.doi.org/10.2118/170144-MS.
MATLAB 2009. Version 7.9.0 (R2009b). Natick, Massachusetts: The MathWorks Inc.
Masih, S., Ma, K., Sanchez, J. et al. 2012. The Effect of Bottom Water Coning and Its Monitoring for Optimization in SAGD. Presented at the SPE Heavy Oil Conference, Calgary, Alberta, Canada, 12–14 June. SPE-157797-MS. http://dx.doi.org/10.2118/157797-MS.
Mohebati, M. H., Maini, B. B., and Harding, T. G. 2010. Optimization of Hydrocarbon Additives With Steam in SAGD for Three Major Canadian Oil Sands Deposits. Presented at the Canadian Unconventional Resources and International Petroleum Conference, Calgary, Alberta, Canada, 19–21 October. SPE-138151-MS. http://dx.doi.org/10.2118/138151-MS.
Morgan, J. T. and Gordon, D. T. 1970. Influence of Pore Geometry on Water-Oil Relative Permeability. J Pet Technol 22 (10): 1199–1208. SPE-2588-PA. http://dx.doi.org/10.2118/2588-PA.
Morrow, R. and Harris, C. C. 1965. Capillary Equilibrium in Porous Materials. SPE J. 5 (1): 15–24. SPE-1011-PA. http://dx.doi.org/10.2118/1011-PA.
Nasr, T. N., Law, D. H. S., Beaulieu, G. et al. 2000. SAGD Application in Gas Cap and Top Water Oil Reservoirs. Presented at the Canadian International Petroleum Conference, 4–8 June, Calgary, Alberta, Canada. SPE-03-01-02-MS. http://dx.doi.org/10.2118/03-01-02-MS.
Nasr, T. N. and Ayodele, O. R. 2006. New Hybrid Steam-Solvent Process for the Recovery of Heavy Oil and Bitumen. Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 5–8 November. SPE-101717-MS. http://dx.doi.org/10.2118/101717-MS.
Nguyen, H. X., Bae, W., Xuan, V. T. et al. 2012. Effects of Reservoir Parameters and Operational Design on the Prediction of SAGD Performance in Athabasca Oil Sands. Presented at the SPE Europec/EAGE Annual Conference, Copenhagen, Denmark, 4–7 June. SPE-154778-MS. http://dx.doi.org/10.2118/154778-MS.
Pooladi-Darvish, M. and Mattar, L. 2002. SAGD Operations in the Presence of Overlying Gas Cap and Water Layer–Effect of Shale Layers. J Can Pet Technol 41 (6): 40–51. SPE-02-06-04-PA. http://dx.doi.org/10.2118/02-06-04-PA.
Reis, J. C. 1992. A Steam-Assisted Gravity Drainage Model for Tar Sands: Linear Geometry. J Can Pet Technol 31 (10): 14–20. SPE-92-10-01-PA. http://dx.doi.org/10.2118/92-10-01-PA.
Ricardo, M. 2013. Simulation Sensitivity Study and Design Parameters Optimization of SAGD Process. Presented at the SPE Heavy Oil Conference, Calgary, Alberta, Canada, 11–13 June. SPE-165387-MS. http://dx.doi.org/10.2118/165387-MS.
Shahab, M. 1995. Neural Network: What It Can Do for Petroleum Engineers. SPE J. 47 (1): 42–42. SPE-29219-PA. http://dx.doi.org/10.2118/29219-PA.
Shahab, M. 2000. Virtual-Intelligence Applications in Petroleum Engineering: Part 1–Artificial Neural Networks. J Pet Technol 52 (9): 64–73. SPE-58046-PA. http://dx.doi.org/10.2118/58046-PA.
Sharma, J. and Gates, I. D. 2011. Convection at the Edge of a Steam-Assisted-Gravity-Drainage Steam Chamber. SPE J. 16 (3): 503–512. SPE-142432-PA. http://dx.doi.org/10.2118/142432-PA.
Skjaeveland, S. M., Siqveland, L. M., Kjosavik, A. et al. 2000. Capillary Pressure Correlation for Mixed-Wet Reservoirs. SPE Res Eval. & Eng. 3 (1): 60–67. SPE-60900-PA. http://dx.doi.org/10.2118/60900-PA.
Smith, D. G., Hubbard, S. M., Leckie, D. A. et al. 2009. Counter Point Bar Deposits: Lithofacies and Reservoir Significance in the Meandering Modern Peace River and Ancient McMurray Formation, Alberta, Canada. Sedimentology 56 (6): 1655–1669. http://dx.doi.org/10.1111/j.1365-3091.2009.01050.x.
Towson, D. E. 1977. Importance of Reservoir Description in Evaluating In Situ Recovery Methods for Cold Lake Heavy Oil–Part II: In Situ Application. J Can Pet Technol 16 (1): 48–54. SPE-77-01-04-PA. http://dx.doi.org/10.2118/77-01-04-PA.
Vanegas, J. W. P., Deutsch, C. V., and Cunha, L. B. 2008. Uncertainty Assessment of SAGD Performance Using a Proxy Model Based on Butler’s Theory. Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 21–24 September. SPE-115662-MS. http://dx.doi.org/10.2118/115662-MS.
Wang, J., Dong, M., and Asghari, K. 2006. Effects of Oil Viscosity on Heavy-Oil/Water Relative Permeability Curves. Presented at the SPE/DOE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, USA, 22–26 April. SPE-99763-MS. http://dx.doi.org/10.2118/99763-MS.
Weiss, W. W., Balch, R. S., and Stubbs, B. A. 2002. How Artificial Intelligence Methods Can Forecast Oil Production. Paper presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, 13–17 April. SPE-75143-MS. http://dx.doi.org/10.2118/75143-MS.
Yang, G. and Butler, R. M. 1992. Effects of Reservoir Heterogeneities on Heavy Oil Recovery by Steam-Assisted Gravity Drainage. J Can Pet Technol 31 (8): 37–43. SPE-92-08-03-PA. http://dx.doi.org/10.2118/92-08-03-PA.
Zhao, L., Anderson, D. B., and O’Rourke, C. 2007. Understanding SAGD Producer Wellbore/Reservoir Damage Using Numerical Simulation. J Can Pet Technol 46 (1): 50–55. SPE-07-01-06-PA. http://dx.doi.org/10.2118/07-01-06-PA.
Zupan, J. 1994. Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them. Acta Chimica Slovenica 41 (3): 327–352.