石油学报 ›› 2011, Vol. 32 ›› Issue (6): 1031-1036.DOI: 10.7623/syxb201106016

• 油田开发 • 上一篇    下一篇

基于改进随机扰动近似算法的油藏生产优化

赵  辉 1  曹  琳 1  李  阳 2  姚  军 2   

  1. 1 长江大学石油工程学院  湖北荆州  434023; 2中国石油大学石油工程学院  山东青岛  266555
  • 收稿日期:2011-05-10 修回日期:2011-08-18 出版日期:2011-11-25 发布日期:2012-01-17
  • 通讯作者: 赵 辉
  • 作者简介:赵 辉,男,1984年3月生,2011年获中国石油大学(华东)油气田开发专业博士学位,现为长江大学石油工程学院教师,主要从事油气田开发、油藏工程及优化控制工程方面的研究。
  • 基金资助:

    国家科技重大专项(2008ZX05024-004)资助。

Production optimization of oil reservoirs based on an improved simultaneous perturbation stochastic approximation algorithm

ZHAO Hui 1  CAO Lin 1  LI Yang 2  YAO Jun 2   

  • Received:2011-05-10 Revised:2011-08-18 Online:2011-11-25 Published:2012-01-17

摘要:

油藏生产优化属于大规模系统最优控制问题,鉴于伴随梯度类算法求解该问题过于复杂的局限性,通过引入控制变量协方差矩阵,提出了一种改进随机扰动近似求解算法(GSPSA)。该算法可对控制变量进行同步扰动获得搜索方向,计算简便,便于和任意油藏模拟器相结合,且搜索方向恒为上山方向,保证了算法的收敛性。计算实例表明,该算法收敛速度快,优化所得方案有效的改善了注水开发效果,且便于实际操作,同时该方法还可被进一步应用于注气驱或三次采油等开发方案的优化及制定。

关键词: 生产优化, 最优控制, 控制变量, 协方差矩阵, 随机扰动近似

Abstract:

Production optimization of oil reservoirs is an issue of large-scale dynamically optimal control. Due to the limitation of being far complicated to have dealt with this issue by the adjoint-gradient-based algorithm, an improved simultaneous perturbation stochastic approximation (SPSA) algorithm was proposed for production optimization by using a prior covariance matrix of control variables. This algorithm is fairly simple and generates the stochastic search direction by perturbing control variables simultaneously, thus it is convenient to be coupled with any commercial numerical simulators and the search direction is always uphill to ensure its final convergence. The application to a synthetic reservoir case indicated that the proposed algorithm could obtain a good convergence performance and the corresponding optimal control was applicable to actual operation, which significantly improved the effect of waterflooding development. This algorithm could also be a promising approach to production optimization of gas injection drive or tertiary oil recovery.

Key words: production optimization, optimal control, control variable, covariance matrix, simultaneous perturbation stochastic approximation