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

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 demandinjection 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 epsilon-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. (C) 2019 Elsevier Ltd. All rights reserved.