基于深度决定性政策梯度的配置控制和石油排水方案参数的优化
Chaodong Tan1,2, Chunqiu Wang2, Jinjie Tian3
1Department of Automation, China University of Petroleum, Changping, Beijing 102249, China.
ACS omega
|July 10, 2023
概括
本研究引入了深度决定性政策梯度 (DDPG) 模型,以优化配置控制和油位移 (PCOD) 参数,显著提高油田的石油生产和回收效率.
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
- 增强的石油回收 (EOR)
背景情况:
- 有效的配置控制和油位移 (PCOD) 参数设计对于提高洪水效率和最大限度地提高油田生产和回收至关重要.
- 传统的优化方法可能无法完全捕捉到PCOD过程的复杂动态.
- 需要先进的计算方法来优化PCOD参数以改善石油回收是显而易见的.
研究的目的:
- 开发和验证使用深度决定性政策梯度 (DDPG) 的PCOD方案的参数优化模型.
- 为了最大限度地利用半年增加的石油产量 (Q) 的注入井组.
- 为了优化PCOD系统类型,度,注射量和注射速率在定义的约束条件下.
主要方法:
- 构建了一个基于 DDPG.G 的参数优化模型和解决方法.
- 利用PCOD历史数据和极端梯度提升 (XGBoost) 来创建PCOD过程的代理模型作为环境.
- 定义了基于Q的变化速率的奖励函数,以系统类型,度,注射量和速率为动作,并采用了高斯式策略,用于探索噪音.
主要成果:
- 基于DDPG的模型成功地优化了海上油田实例中复合污泥PCOD过程 (预污泥+主污泥+保护污泥) 的参数.
- 对于具有不同PCOD特征的井组,实现了更高的石油产量PCOD方案.
- 与粒子群优化 (PSO) 模型相比,展示了优越的优化和概括能力.
结论:
- 基于DDPG的参数优化模型对于提高PCOD方案性能是有效的.
- 拟议的方法在优化油田生产和回收方面具有显著的优势.
- DDPG方法在复杂的水库管理场景中显示出强大的实际应用潜力.
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01:22Controller Configurations
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01:19PID Controller
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01:17Rolling Resistance: Problem Solving
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