【摘要】 针对特高含水后期综合调整方案考虑因素多、实现产出投入比最大化难的问题,运用粒子群算法,以增油量目标、增液量目标、增注量目标、作业费用等为约束条件,建立了基于粒子群算法的综合调整方案多目标优化模型。将研究成果应用到大庆杏南油田综合调整年度方案中,利用文中方法比人工优化少实施16井次,年度多增油0. 2×104t,年度少产液0. 2×104t,自然递减比计划低0. 01个百分点,年均含水比计划低0. 03个百分点,产出投入比由过去的2. 61∶1. 00提高至2. 91∶1. 00,取得了较好的效果。研究成果对提高综合调整方案的质量和效率具有重要指导意义。更多还原

【Abstract】 In order to improve the problems of multiple influencing factors and relatively low input-output ratio of the comprehensive adjustment program in the ultra-high water-cut development stage,the oil increment,liquid increment,injection increment,operation cost and other factors are taken as the constrains to establish a multi-objective optimization model based on particle swarm optimization. This optimization is applied in the annual comprehensive adjustment program of Daqing Xingnan Oilfield. Comparing to the early manual optimization,the operation times were reduced by 16 well-times,the annual oil increment is 0. 2× 10~4 t,the annual liquid production is reduced by 0. 2×10~4 t,the natural production decline rate is reduced by 0. 01 percentages,the annual average watercut is reduced by 0. 03 percentages and the input-output ratio is enhanced from 2. 61 ∶ 1. 00 to 2. 91 ∶ 1. 00,which shows a favorable optimization performance. This research could provide certain guidance for improving the quality and efficiency of the comprehensive adjustment program.更多还原