基于GRU-Informer的ROP的实时预测
Bingrui Tu1,2,3, Kai Bai4,5,6, Ce Zhan1,2,3
1Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Wuhan, China.
Scientific reports
|January 25, 2024
概括
本研究介绍了GRU-Informer模型用于实时透率 (ROP) 预测. 该模型通过捕捉短期和长期钻井参数依赖性,准确地预测ROP.
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
- 机器学习 机器学习
背景情况:
- 准确的透率 (ROP) 预测对于优化石油和天然气生产运营和降低成本至关重要.
- 现有的ROP预测方法通常依赖于历史数据,并与实时预测作斗争.
- 钻探参数的复杂性对精确的ROP预测构成了重大挑战.
研究的目的:
- 开发一种用于准确实时ROP预测的新型模型.
- 提高生产任务规划和运营管理的效率.
- 解决当前方法在捕获钻探数据中短期和长期依赖性的局限性.
主要方法:
- 引入GRU-Informer模型,将Gated Recurrent Unit (GRU) 网络用于短期相关性和Informer模型用于长期依赖性.
- 利用来自中国西南部油田的数据集进行模型培训和验证.
- 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和确定系数 (R2) 作为评估指标.
主要成果:
- 与LSTM,GRU和Informer等传统模型相比,GRU-Informer模型在实时ROP预测中表现出卓越的性能.
- 该模型有效地捕捉了钻探参数中的短期和长期时间依赖.
- 实验结果验证了拟议的GRU-Informer方法的实际价值和准确性.
结论:
- GRU-Informer模型在实时ROP预测准确度方面取得了重大进展.
- 这种方法提高了管理和优化石油和天然气钻探运营的能力.
- 该模型能够整合短期和长期依赖性,从而提供更强大的预测能力.
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