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

Baehyun Min, Krupa Kannan and Sanjay Srinivasan
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Baehyun Min: 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
Krupa Kannan: Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, TX 78712, USA
Sanjay Srinivasan: Department of Energy and Mineral Engineering, College of Earth and Mineral Sciences, Pennsylvania State University, University Park, PA 16802, USA

Energies, 2017, vol. 10, issue 7, 1-21

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.

Keywords: oil sands; trade-off; ES-SAGD; Pareto-optimality; surrogate model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
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