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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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Figures

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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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