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Energies 2017, 10(7), 966; doi:10.3390/en10070966

Quick Screening of Pareto-Optimal Operating Conditions for Expanding Solvent–Steam Assisted Gravity Drainage Using Hybrid Multi-Objective Optimization Approach

1
Department of Climate and Energy Systems Engineering, Division of Sustainable Systems Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Daehyeon-dong, Seodaemun-gu, Seoul 03760, Korea
2
Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, TX 78712, USA
3
Department of Energy and Mineral Engineering, College of Earth and Mineral Sciences, Pennsylvania State University, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Received: 20 April 2017 / Revised: 18 June 2017 / Accepted: 5 July 2017 / Published: 10 July 2017
(This article belongs to the Section Energy Sources)
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Abstract

Solvent–steam mixture is a key factor in controlling the economic efficiency of the solvent-aided thermal injection process for producing bitumen in a highly viscous oil sands reservoir. This paper depicts a strategy to quickly provide trade-off operating conditions of the Expanding Solvent–Steam Assisted Gravity Drainage (ES-SAGD) process based on Pareto-optimality. Response surface models are employed to evaluate multiple ES-SAGD scenarios at low computational costs. The surrogate models play a role of objective-estimators in the multi-objective optimization that provides qualified ES-SAGD scenarios regarding bitumen recovery, steam–energy efficiency, and solvent-energy efficiency. The developed hybrid approach detects positive or negative correlations among the performance indicators of the ES-SAGD process. The derived Pareto-optimal operating conditions give flexibility in field development planning and thereby help decision makers determine the operating parameters of the ES-SAGD process based on their preferences. View Full-Text
Keywords: oil sands; trade-off; ES-SAGD; Pareto-optimality; surrogate model oil sands; trade-off; ES-SAGD; Pareto-optimality; surrogate model
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Min, B.; Kannan, K.; Srinivasan, S. Quick Screening of Pareto-Optimal Operating Conditions for Expanding Solvent–Steam Assisted Gravity Drainage Using Hybrid Multi-Objective Optimization Approach. Energies 2017, 10, 966.

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