基于改进粒子群算法的蒸汽驱注采方案优化

doi:10.7623/syxb201401013

石油学报 ›› 2014, Vol. 35 ›› Issue (1): 114-117,140.DOI: 10.7623/syxb201401013

基于改进粒子群算法的蒸汽驱注采方案优化

倪红梅^1,2, 刘永建¹, 范英才³, 李盼池²

1. 东北石油大学提高油气采收率教育部重点实验室黑龙江大庆 163318;
2. 东北石油大学计算机与信息技术学院黑龙江大庆 163318;
3. 中国石油辽河油田公司辽宁盘锦 124010

收稿日期:2013-08-08 修回日期:2013-10-11 出版日期:2014-01-25 发布日期:2013-12-09
通讯作者: 倪红梅，女，1975年4月生，1998年毕业于大庆石油学院计算机软件专业，现为东北石油大学副教授，主要从事提高采收率、智能优化等方面的研究。Email：nhm257@163.com
作者简介:倪红梅，女，1975年4月生，1998年毕业于大庆石油学院计算机软件专业，现为东北石油大学副教授，主要从事提高采收率、智能优化等方面的研究。Email：nhm257@163.com
基金资助:
国家重大科技专项（2011ZX05012-003）、国家自然科学基金项目（No.61170132）和黑龙江省教育厅科学技术研究项目（12521058）资助。

Injection and production project optimization of steam flooding based on improved particle swarm optimization algorithm

Ni Hongmei^1,2, Liu Yongjian¹, Fan Yingcai³, Li Panchi²

1. State Key Laboratory of Enhanced Oil & Gas Recovery of Ministry of Education, Northeast Petroleum University, Daqing 163318, China;
2. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;
3. PetroChina Liaohe Oilfield Company, Panjin 124010, China

Received:2013-08-08 Revised:2013-10-11 Online:2014-01-25 Published:2013-12-09

摘要/Abstract

摘要： 针对蒸汽驱注采方案优化问题，以累积油汽比为优化目标，建立了以注汽速率、蒸汽干度和注汽压力等为约束条件的蒸汽驱注采方案优化数学模型，采用改进粒子群算法对该模型进行了求解，并优化了蒸汽驱的主要注采参数。改进粒子群算法以进化停滞步数为依据，对个体历史最优值和邻域内粒子的最优值实施随机扰动，并且只接受使个体适应度增加的随机扰动操作，使记忆中的最优粒子跳出局部最优解，保证了种群的多样性，提高了算法的精度和稳定性。实例计算证明，该优化模型计算结果准确，优化算法有效。通过该优化方法可快捷准确地进行蒸汽驱动态优化和方案调整，以便于指导蒸汽驱高效运行。

关键词: 蒸汽驱, 注采方案优化, 粒子群算法, 随机扰动, 数学模型

Abstract: A mathematic model for injection and production optimization of steam flooding project was established by taking cumulative oil steam ratio as the optimization objective, and steam injection rate, steam quality, and steam injection pressure as the constraint conditions. An improved particle swarm optimization algorithm was used to solve the proposed model and to optimize the major injection and production parameters of steam flooding. The optimal values of individual history and neighborhood particles were disturbed randomly using the optimization algorithm according to the evolutionary stagnation steps; only the operation of random disturbance with increased individual fitness was retained so that local optimal solution could jump out with the optimal particle in the memory, thereby ensuring the population diversity and improving the algorithm's precision and stability. The optimization model was proven accurate and the optimization algorithm was validated in a case study. The proposed methodology enabled real-time and accurate dynamic optimization and project adjustment of steam flooding, ultimately contributing to its efficient operation.

Key words: steam flooding, injection and production project optimization, particle swarm optimization algorithm, random disturbance, mathematic model

中图分类号:

TE357.44

倪红梅, 刘永建, 范英才, 李盼池. 基于改进粒子群算法的蒸汽驱注采方案优化[J]. 石油学报, 2014, 35(1): 114-117,140.

Ni Hongmei, Liu Yongjian, Fan Yingcai, Li Panchi. Injection and production project optimization of steam flooding based on improved particle swarm optimization algorithm[J]. ACTA PETROLEI SINICA, 2014, 35(1): 114-117,140.

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基于改进粒子群算法的蒸汽驱注采方案优化

Injection and production project optimization of steam flooding based on improved particle swarm optimization algorithm

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