A novel approach for optimization of polymer-surfactant flooding based on simultaneous perturbation stochastic approximation algorithm

作者：Zhang Kai; Zhang Xiuqing; Zhang Liming; Zhang Jin; Sun Hai; Huang Zhaoqin; Yao Jun

来源：Journal of China University of Petroleum(Edition of Natural Science), 2017, 41(5): 102-109.

DOI：10.3969/j.issn.1673-5005.2017.05.012

• Normalized gradient
• Optimal injection
• Polymer surfactants
• Production optimization
• Random and different step
• Simultaneous perturbation stochastic approximation algorithms

Polymer-surfactant flooding, as a kind of effective method to enhance oil recovery, has been used in oilfield widely. However, at the current low crude oil price scenarios, how to maximize the production benefit (or project economics) has become an emergent problem to solve. In view of this, in this study, the production benefit was taken as the optimization target, and the polymer and surfactant concentrations, injection volume, injection time were considered as control variables, in order to construct a novel simultaneous perturbation stochastic approximation algorithm, which was improved by a random and differential step method to optimize the production benefit. Two case studies were carried out, with and without restriction on the usage of polymer and surfactant. The simulation results indicate that the improved simultaneous perturbation stochastic approximation algorithm not only can speed up convergence but also can get better optimal result. When the usage amount of polymer and surfactant are not restricted, the production benefits will be better if the polymer and surfactant are injected at lower water cut. When the usage amount of polymer and surfactant are restricted, the production benefits will be better if the polymer and surfactant are injected at higher water cut.

• 出版日期2017
• 单位中国石油大学（华东）