Research Papers: Petroleum Engineering

A Proxy Model for Predicting SAGD Production From Reservoirs Containing Shale Barriers

[+] Author and Article Information
Jingwen Zheng

Department of Civil & Environmental Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada

Juliana Y. Leung

Department of Civil & Environmental Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada

Ronald P. Sawatzky, Jose M. Alvarez

InnoTech Alberta,
Edmonton, AB T6N 1E4, Canada

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received November 10, 2017; final manuscript received July 23, 2018; published online August 30, 2018. Assoc. Editor: Daoyong (Tony) Yang.

J. Energy Resour. Technol 140(12), 122903 (Aug 30, 2018) (10 pages) Paper No: JERT-17-1632; doi: 10.1115/1.4041089 History: Received November 10, 2017; Revised July 23, 2018

Artificial intelligence (AI) tools are used to explore the influence of shale barriers on steam-assisted gravity drainage (SAGD) production. The data are derived from synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints gathered from the Suncor's Firebag project, which is representative of Athabasca oil sands reservoirs. The underlying reservoir simulation model is homogeneous and two-dimensional. Reservoir heterogeneities are modeled by superimposing sets of idealized shale barrier configurations on this homogeneous reservoir model. The individual shale barriers are categorized by their location relative to the SAGD well pair and by their geometry. SAGD production for a training set of shale barrier configurations was simulated. A network model based on AI tools was constructed to match the output of the reservoir simulation for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for arbitrary configurations of shale barriers. The predicted results were consistent with the results of the SAGD simulation model with the same shale barrier configurations. The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parametrization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from SAGD field production data.

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Butler, R. , McNab, G. , and Lo, H. , 1981, “Theoretical Studies on the Gravity Drainage of Heavy Oil During in‐Situ Steam Heating,” Can. J. Chem. Eng., 59(4), pp. 455–460. [CrossRef]
Birrel, G. E. , and Putnam, P. E. , 2000, “A Study of the Influence of Reservoir Architecture on SAGD Steam Chamber Development at the Underground Test Facility, Northeaster Alberta, Canada, Using a Graphical Analysis Of Temperature Profiles,” Petroleum Society's Canadian International Petroleum Conference, Calgary, AB, Canada, Paper No. PETSOC-2000-104.
Zhang, W. , Youn, S. , and Doan, Q. T. , 2007, “Understanding Reservoir Architectures and Steam-Chamber Growth at Christina Lake, Alberta, by Using 4D Seismic and Crosswell Seismic Imaging,” SPE Reservoir Eval. Eng., 10(5), pp. 446–452.
Yang, G. , and Butler, R. , 1992, “Effects of Reservoir Heterogeneities on Heavy Oil Recovery by Steam-Assisted Gravity Drainage,” J. Can. Pet. Technol., 31(8), pp. 37–43.
Chen, Q. , Gerritsen, M. G. , and Kovscek, A. R. , 2008, “Effects of Reservoir Heterogeneities on the Steam-Assisted Gravity-Drainage Process,” SPE Reservoir Eval. Eng., 11(5), pp. 921–932. [CrossRef]
Amirian, E. , Leung, J. Y. , Zanon, S. , and Dzurman, P. , 2015, “Integrated Cluster Analysis and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Performance Prediction in Heterogeneous Reservoirs,” Expert Syst. Appl., 42(2), pp. 723–740. [CrossRef]
Wang, C. , and Leung, J. , 2015, “Characterizing the Effects of Lean Zones and Shale Distribution in Steam-Assisted-Gravity-Drainage Recovery Performance,” SPE Reservoir Eval. Eng., 18(3), pp. 329–345. [CrossRef]
Lee, H. , Jin, J. , Shin, H. , and Choe, J. , 2015, “Efficient Prediction of SAGD Productions Using Static Factor Clustering,” ASME J. Energy Resour. Technol., 137(3), p. 032907. [CrossRef]
Elkatatny, S. , 2018, “Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data,” ASME J. Energy Resour. Technol., 140(7), p. 072905. [CrossRef]
Le Van, S. , and Chon, B. H. , 2018, “Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks,” ASME J. Energy Resour. Technol., 140(3), p. 032906. [CrossRef]
Moussa, T. , Elkatatny, S. , Mahmoud, M. , and Abdulraheem, A. , 2018, “Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches,” ASME J. Energy Resour. Technol., 140(7), p. 072903. [CrossRef]
Haykin, S. , 2008, Neural Networks and Learning Machines, 3rd ed., M. J. Horton , S. Disanno , eds., Prentice Hall, Upper Saddle River, NJ.
Ma, Z. , Leung, J. Y. , Zanon, S. , and Dzurman, P. , 2015, “Practical Implementation of Knowledge-Based Approaches for Steam-Assisted Gravity Drainage Production Analysis,” Expert Syst. Appl., 42(21), pp. 7326–7343. [CrossRef]
Fedutenko, E. , Yang, C. , Card, C. , and Nghiem, L. X. , 2014, “Time-Dependent Neural Network Based Proxy Modeling of SAGD Process,” SPE Heavy Oil Conference-Canada, Calgary, AB, Canada, June 10–12, SPE Paper No. SPE-170085-MS.
Ma, Z. , Leung, J. Y. , and Zanon, S. , 2017, “Practical Data Mining and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Production Analysis,” ASME J. Energy Resour. Technol., 139(3), p. 032909. [CrossRef]
IHS Energy, 2015, “AccuMap Software,” 321 Inverness Drive South Englewood, CO, accessed Apr. 23, 2018, http://www.ihsenergy.com
TOP Analysis, 2015, “TOP Analysis Software,” TOP Analysis, Calgary, AB, Canada, accessed Nov. 17, 2015, http://www.topanalysis.com
Regulator, A.E., 2012, “Suncor Firebag 2012 ERCB Performance Presentation,” Alberta Energy Regulator, Calgary, AB, Canada, accessed Mar. 15, 2016, https://www.aer.ca/documents/oilsands/insitu-presentations/2012AthabascaSuncorFirebagSAGD8870.pdf
Regulator, A. E. , 2013, “Suncor Firebag 2013 ERCB Performance Presentation,” Alberta Energy Regulator, Calgary, AB, Canada, accessed Mar. 15, 2016, https://www.aer.ca/documents/oilsands/insitu-presentations/2013AthabascaSuncorFirebagSAGD8870.pdf
Regulator, A. E. , 2014, “Suncor Firebag 2014 AER Performance Presentation,” Alberta Energy Regulator, Calgary, AB, Canada, accessed Mar. 15, 2016, https://www.aer.ca/documents/oilsands/insitu-presentations/2014AthabascaSuncorFirebagSAGD8870.pdf
Li, P. , 2006, “Numerical Simulation of the SAGD Process Coupled With Geomechanical Behavior,” Ph.D. thesis, University of Alberta, Edmonton, AB, Canada.
CMG, 2015, STARS: Users' Guide, Advanced Processes & Thermal Reservoir Simulator (Version 2015), Computer Modeling Group, Calgary, AB, Canada.
Cox, T. F. , and Cox, M. A. , 2001, Multidimensional Scaling, 2nd ed., Chapman and Hall, London, UK.
Zheng, J. , Leung, J. Y. , Sawatzky, R. P. , and Alvarez, J. M. , 2018, “A Cluster-Based Approach for Visualizing and Quantifying the Uncertainty in the Impacts of Uncertain Shale Barrier Configurations on SAGD Production,” SPE Canada Heavy Oil Technical Conference, Calgary, AB, Canada, SPE Paper No. SPE-189753-MS.
Nielsen, M. A. , 2015, Neural Network and Deep Learning, Determination Press.
Deutsch, C. V. , and Journel, A. G. , 1998, GSLIB: Geostatistical Software Library and User's Guide, Oxford University Press, New York.
Liu, J. , Jaiswal, A. , Yao, K. , and Raghavenda, C. S. , 2015, “Autoencoder-Derived Features as Inputs to Classification Algorithms for Predicting Well Failures,” SPE Western Regional Meeting, Garden Grove, CA, SPE Paper No. SPE-174015-MS.


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Fig. 1

Relative pad locations for the Firebag project—image extracted using AccuMap [16]

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Fig. 2

Relative permeability functions: (a) water–oil and (b) liquid–gas

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Fig. 3

Viscosity functions with temperature: (a) oil and (b) methane

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Fig. 4

Comparison of monthly oil production between the base model and field observations

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Fig. 5

Illustration of the four domains and the corresponding basic shale unit

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Fig. 6

Temperature and gamma ray profiles for the observation well 87 in Firebag—data are extracted from Suncor's Firebag annual reports [18,19]

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Fig. 7

Representative regions for the models: (a) laterally extensive shale barriers and (b) vertically extensive shale barriers

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Fig. 8

Schematic of the MLP network architecture used in this study with one hidden layer

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Fig. 9

Sample testing cases: (a) shale configuration, (b) monthly steam injection and oil production, and (c) SOR. ANN: proposed ANN model result and CMG: CMG simulation result.

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Fig. 10

Validation cases: (a) original shale configuration, (b) parameterized shale configuration, and (c) monthly steam injection and oil production. Original: CMG simulation result of the original case, parameterized: CMG simulation result of the parameterized case, and ANN: proposed ANN model result of the parameterized case.

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Fig. 11

Oil saturation distribution of validation case 1: (a) original shale configuration and (b) parameterized shale configuration

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Fig. 12

Stacking of 2D models in three-dimensional (3D)



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