一个混合AI-基因算法框架,用于优化聚合物泛滥策略:基于数值模拟的方法
Milad Nourizadeh1, Razieh Khosravi1, Mohammad Simjoo2
1Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran.
Scientific reports
|January 19, 2026
概括
本研究介绍了一种AI-Genetic Algorithm (GA) 框架,用于优化聚合物泛滥,增强石油回收. 混合方法使用神经网络加速模拟,揭示最大限度地提高石油生产和利的关键参数.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能在能源中的作用
- 储水池模拟器 储水池模拟器
背景情况:
- 正在减少的传统石油资源需要先进的回收技术.
- 聚合物泛滥对于增强石油回收至关重要,但由于复杂的参数和高仿真成本,它面临着优化挑战.
研究的目的:
- 开发一个高效的混合AI-遗传算法 (GA) 框架,以优化聚合物泛滥.
- 使用机器学习代理模型克服传统水库模拟的计算局限性.
主要方法:
- 创建一个960个案例的核心规模模拟数据集.
- 推进神经网络 (FNN) 和埃尔曼循环神经网络 (E-RNN) 代理模型的开发.
- 将高保真性E-RNN代理与GA连接起来,以实现多目标优化.
主要成果:
- 在预测动态生产数据方面,E-RNN代理模型实现了高精度 (R2 > 0.99).
- 为了最大限度地回收石油,优化的参数包括高透性,注入速率和聚合物度,使异质性最小化.
- 经济优化表明,人们更喜欢短,密集的注入期来最大限度地提高利,这表明了技术经济的权衡.
结论:
- 混合AI-GA框架有效地加速了聚合物泛滥设计和优化.
- 该研究强调了在优化策略中考虑技术和经济因素的重要性.
- 未来的工作包括整合实验室数据,并将框架扩展到全场应用.
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