对低盐度聚合物洪水试点规模开发的新见解,使用基于智能代理模型的智能代理模型与粒子群集优化相结合
Razieh Khosravi1, Mohammad Simjoo2, Mohammad Chahardowli1
1Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran.
Scientific reports
|November 23, 2024
概括
这项研究开发了一种人工智能代理模型,用于重油库中低盐度聚合物泛滥. 该模型优化了注射速率和盐度等参数,以提高石油回收和经济可行性.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能在水库管理中的应用
背景情况:
- 不同质的重油库对低盐度聚合物泛滥构成挑战.
- 了解盐度,聚合物和水库相互作用是成功实施的关键.
研究的目的:
- 开发一种人工智能驱动的代理模型,以优化异质重油储库中低盐度聚合物泛滥.
- 为了确定影响洪水性能和预测结果的关键参数.
主要方法:
- 结合神经网络和粒子群优化算法.
- 使用来自试点规模动态模拟器的数据来训练代理模型.
- 识别有影响力的参数和优化输入变量.
主要成果:
- 代理模型在预测石油回收,水切割和底孔压力方面取得了很高的准确性 (R2=0.996,MSE=0.0011).
- 注射速率被确定为最有影响力的参数;聚合物度是最不显著的.
- 优化的参数包括注射速率 (1450bbl/day),盐度 (4000ppm) 和聚合物度 (1500ppm).
结论:
- 人工智能代理模型是优化复杂水库中低盐度聚合物洪水的一个有价值的工具.
- 优化策略平衡了业绩提升与经济可行性和注入性.
- 这种方法可以显著改善油田开发中的基于水的回收方法.
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