动态静态神经网络模型的应用 整合物理约束 在欧元中预测页岩气井的预测
Ye Li1,2, Zhiyang Pi1,2, Gang Hui1
1State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China.
ACS omega
|January 8, 2026
概括
这项研究引入了一种混合机器学习模型,用于预测页岩油库中最终的石油回收. 该框架通过整合地质数据与生产历史来提高准确性,改善投资决策.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习应用 机器学习应用
- 地质科学是地球科学.
背景情况:
- 精确估计最终的回收 (欧元) 对页岩水库开发至关重要.
- 由于复杂的地质和生产动态,EUR的预测具有挑战性.
研究的目的:
- 开发一种混合机器学习框架,用于在页岩水库中增强欧元预测.
- 为了提高准确性,将静态地质参数与动态生产数据相结合.
主要方法:
- 一个混合机器学习框架,将静态地质数据和动态生产数据结合起来.
- 纳入双重物理约束机制 (阿普斯衰减方程,达西定律).
- 实施动态权重策略,以平衡基于生产阶段的特征贡献.
主要成果:
- 实现了0.85的完整生产历史和0.83的有限数据的R2,比静态方法有5.7%的改进.
- 使用仅20个月的生产数据和静态参数,证明了高预测准确性 (R2 = 0.83).
- 在Duvernay页岩形成中验证的有效性.
结论:
- 混合模型为欧元预测提供了可靠的工具,特别是在数据稀缺的情况下.
- 这种方法支持明智的投资决策和页岩油田的优化开发策略.
- 该框架显示了边缘或未开发油田的巨大潜力.
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