GA与分层优化结合的掺水集油工艺参数优化
Optimization of water blending and oil gathering process parameters by integrating Genetic Algorithm and hierarchical optimization
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- 引用格式:
-
成庆林,谢宁,孟岚,孙巍,李治东,刘悦.GA与分层优化结合的掺水集油工艺参数优化[J].天然气与石油,2024,42(3):31-39.doi:10.3969/j.issn.1006-5539.2024.03.006
CHENG Qinglin, XIE Ning, MENG Lan, SUN Wei, LI Zhidong, LIU Yue.Optimization of water blending and oil gathering process parameters by integrating Genetic Algorithm and hierarchical optimization[J].Natural Gas and Oil,2024,42(3):31-39.doi:10.3969/j.issn.1006-5539.2024.03.006
- DOI:
- 10.3969/j.issn.1006-5539.2024.03.006
- 作者:
- 成庆林1 谢宁1 孟岚2 孙巍1 李治东1 刘悦1
CHENG Qinglin1, XIE Ning1, MENG Lan2, SUN Wei,1 LI Zhidong1, LIU Yue1
- 作者单位:
- 1. 东北石油大学提高采收率教育部重点实验室, 黑龙江 大庆 163318; 2. 大庆油田工程有限责任公司, 黑龙江 大庆 163000
1. Key Laboratory of Improving Oil and Gas Recovery of Ministry of Education, Northeast Petroleum University,Daqing, Heilongjiang, 163318, China; 2. Daqing Oilfield Engineering Co., Ltd., Daqing, Heilongjiang, 163000, China
- 关键词:
- 集油能耗;掺水集油工艺;工艺参数;分层优化;GA
Oil gathering energy consumption; Water blending and oil gathering process; Operating parameters; Hierarchical optimization; Genetic Algorithm
- 摘要:
为降低进入特高含水阶段油田的掺水集油能耗,对掺水集油工艺参数进行优化。以掺水温度、掺水量、掺水压力为外层决策变量,加热炉和掺水泵运行方案为内层决策变量,以掺水集油工艺综合能耗最低为目标函数,建立掺水集油工艺参数优化模型。提出了一种遗传算法(Genetic Algorithm，GA)与分层优化结合的求解策略,将掺水温度作为染色体上的基因,先优化个体对应加热炉和掺水泵运行方案,得到个体对应的最小集油能耗,再通过种群的不断迭代进化,得到掺水集油工艺的最优参数。研究结果表明,与优化前相比,掺水温度降低8.3 ℃,掺水量减少 131.6 m3/d,掺水压力降低0.15 MPa,集输吨液综合能耗降低13.64%,优化效果良好。研究成果可为进入特高含水阶段油田降低掺水集油能耗提供借鉴。
To minimize the energy consumption of water blending and oil gathering system in oil fields that have entered the ultra-high water cut phase, an optimization of the operational parameters of water blending and oil gathering process was conducted. The optimization model of the operating parameters of the water blending and oil gathering process was developed with water blending temperature, volume, and pressure as the outer-layer decision variables, and the operation schemes of the heat furnaces and water blending pumps as the inner-layer decision variables, aiming for the lowest possible overall energy consumption in water blending and oil gathering process. A solution strategy integrating Genetic Algorithm (GA) and hierarchical optimization was proposed. The water blending temperature was regarded as a gene on the chromosome. Initially, the operation scheme for individual heat furnace and water blending pump was optimized to obtain the minimum oil gathering energy consumption for each corresponding case. Subsequently, through continuous iterative evolution of the population, the optimal operating parameters for the water blending and oil gathering process were obtained. The results indicate that, compared to pre-optimization levels, the water blending temperature is reduced by 8.3 ℃, the volume of water blending is reduced by 131.6 m3/d, the water blending pressure is reduced by 0.15 MPa, and the overall energy consumption for gathering and transporting each ton of liquid is reduced by 13.64%, demonstrating a favorable optimization outcome. The research results can provide reference for reducing the energy consumption of water blending and oil gathering system in oil fields that have entered the ultra-high water cut phase.
