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Crude oil stabilization is one of the important processes for crude oil treatment, which can reduce the evaporation loss of crude oil and improve the process safety of crude oil storage and transportation. The difference of operating conditions affects the product benefit and energy consumption of crude oil stabilization. In order to improve the overall economic efficiency, this paper used the data interaction function of MATLAB and HYSYS, and the temperature and pressure of the stabilized tower were optimized by using the particle swarm optimization (PSO) algorithm. Meanwhile, combined with the distributed energy system, the mixed-integer linear programming model was developed, and the design strategy of the energy supply equipment was determined by taking the minimum annual equipment cost and fuel cost as the objective function, so as to reduce energy costs and carbon emissions. The method was validated in an oil field in China. The results showed that the optimal operating conditions after optimization were 0.1MPa and 130°C. The energy supply equipment was selected as an electric heater, a CHP engine, and a waste heat boiler. The energy consumption cost at this time isCNY/a, which is 17.65% less than the actual operating cost of the site. At the same time, carbon emissions fell by 7.8%. This method can guide the operation of the oilfield site to a certain extent.

An optimization framework to integrate distributed energy system in the crude oil stabilization process