基于强化学习SAC算法对分层注水的智能调节的研究
Jinzhao Hu1, Deli Jia2, Shichu Liu1
1Northeast Petroleum University, Daqing, 163318, Heilongjiang, China.
Scientific reports
|September 26, 2025
概括
本研究介绍了一种使用强化学习进行分层注入水的智能控制方法,大大提高了油田生产效率. 与其他方法相比,软演员-关键 (SAC) 算法表现出卓越的准确性和速度.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 水注入对于保持油库压力和确保油田生产稳定至关重要.
- 目前用于分层注水的控制方法可能缺乏最佳效率所需的精度.
- 智能控制有助于提高油田运营的性能.
研究的目的:
- 开发和评估使用强化学习对分层注水柱的智能控制方法.
- 为准确的流量控制构建精确的分层注水模型和算法.
- 将拟议方法的性能与现有的强化学习算法进行比较.
主要方法:
- 建立了一个注水柱流量模型,集成设备结构,压力损失和流量特征.
- 一个智能控制系统是使用软演员-关键 (SAC) 算法设计的.
- 使用PyTorch开发了一个模拟平台,用于训练和测试SAC,近距离政策优化 (PPO) 和深度决定性政策梯度 (DDPG) 算法.
主要成果:
- 在5%的门下,SAC算法实现了5%的平均调节错误,超过了PPO (37%).
- SAC的监管资格率为81%,明显高于PPO (45%) 和DDPG (60%).
- SAC需要更少的调整步骤 (比PPO少42%,比DPG少28%) 并显示出更大的稳定性和适应性.
结论:
- SAC算法为智能分层注水提供了卓越的调节精度,合格率和响应速度.
- 这种智能控制方法为提高油田开发效率提供了重要的理论支持和实际价值.
- 开发的代码可以在网上获得,以促进进一步的研究和应用.
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