在CO2-WAG优化中用于智能代理建模的堆积学习:一种技术经济方法来实现可持续的增强石油回收
Mahdi Kanaani1, AliMohammad Sedaghat Kameholiya1, Alireza Amarzadeh2
1Institute of Petroleum Engineering, Department of Chemical Engineering, College of Engineering, University of Tehran, Tehran 1417614418, Iran.
ACS omega
|March 17, 2025
概括
这项研究优化了二氧化碳-水交替气体 (CO2-WAG) 注入,以提高石油回收和碳封存. 先进的机器学习框架实现了可持续能源生产的高效,准确和经济可行的解决方案.
科学领域:
- 石油工程是石油工程中的一个.
- 环境科学 环境科学
- 机器学习 机器学习
背景情况:
- 气候变化需要创新的解决方案来减少碳足迹和能源需求.
- 二氧化碳-水交替气体 (CO2-WAG) 注入提供了增强的石油回收和碳封存的双重好处.
- 优化CO2-WAG过程对于平衡生产,封存和经济可行性至关重要.
研究的目的:
- 开发和验证CO2-WAG注入的综合优化框架.
- 为了同时最大限度地提高石油生产和二氧化碳的封存,同时最大限度地减少水生产和确保经济可行性.
- 为了提高CO2-WAG过程优化中的计算效率和预测准确性.
主要方法:
- 整合机器学习 (堆叠学习) 用于智能代理模型开发.
- 应用非主导排序基因算法II (NSGA-II) 进行多目标优化.
- 利用水库模拟数据在各种条件下进行模型训练和验证.
主要成果:
- 使用NSGA-II.II使用500个最佳解决方案生成3D帕雷托前线.
- 确定提供最好的技术和经济权衡的60个"黄金"解决方案.
- 与以前的方法相比,计算效率和预测准确度的显著提高.
结论:
- 拟议的框架有效地优化了CO2-WAG注入,以提高石油回收和碳管理.
- 该方法为可持续能源生产和碳封存提供了一个可扩展和适应的解决方案.
- 这项研究表明,使用集成的人工智能和优化技术优化复杂的水库过程取得了重大进展.
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