A multi-objective and multi-scenario optimization model for operation control of CO2-flooding pipeline network system

Rui Qiu, Haoran Zhang, Xingyuan Zhou, Zhichao Guo, Guannan Wang, Long Yin, Yongtu Liang

Research output: Contribution to journalArticle

Abstract

Countries with high carbon emissions are actively exploring carbon capture, utilization and storage (CCUS) system. CCUS-based CO2 enhanced oil recovery (CO2-EOR) technology is favored for sustainable oilfield development and its contribution to mitigating global warming. In this paper, under the crafts constraints of injection stations and CO2-flooding wells, as well as the flow rate and pressure constraints along pipeline network, a multi-objective mixed integer nonlinear programming (MOMINLP) model is proposed for the optimal operation control of oilfield surface CO2-flooding pipeline network system. The minimum operating costs of pumps, the maximum CO2 injection volume and the minimum demand-injection volume deviation are set as objective functions. The uncertainty of demand CO2 injection volume caused by geological uncertainty is settled by scenario-based stochastic programming method. In addition, the piecewise linearization method and the augmented ε-constraint method (AUGMECON) are introduced to deal with the nonlinear constraints and get the Pareto optimal solutions, respectively. Finally, the proposed model is successfully applied to a large-scale looped and branched CO2-flooding pipeline network system in Sinkiang, China with three cases for comparison to verify its applicability and superiority.

Original languageEnglish
Article number119157
JournalJournal of Cleaner Production
Volume247
DOIs
Publication statusPublished - 20 Feb 2020

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Keywords

  • CO enhanced oil recovery (CO-EOR)
  • CO-flooding pipeline network
  • Multi-objective
  • Multi-scenario
  • Operation control scheme

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