基于GAN-LSTM的卷状管道钻井的参数预测
Wanxing Zhang1,2, Kai Bai3,4,5, Ce Zhan1,2
1Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University (Ministry of Education and Hubei Province), Wuhan, 430100, Hubei, China.
Scientific reports
|July 5, 2023
概括
本研究引入了一种GAN-LSTM模型,用于预测卷轴管钻井参数,提高运营安全性和效率. 该模型达到90%的准确性,改进了现有的方法,降低了生产成本.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能在钻井中的应用
背景情况:
- 卷轴管道钻井技术提供了显著的优势,但面临着诸如钻井卡住和使用寿命缩短等挑战.
- 钻井参数,制造缺陷和操作处理影响卷轴管的性能.
- 循环压力,井头压力和总重量是影响卷轴管道操作的关键因素.
研究的目的:
- 为关键卷状管道钻井参数开发一个预测模型.
- 提高螺旋管运行中的运行安全和钻井效率.
- 通过改进钻井管理来降低总体生产成本.
主要方法:
- 使用生成对抗网络 (GAN) 结合长期短期记忆 (LSTM) 网络 (GAN-LSTM).
- 综合钻井工程理论,分析广泛的下井数据.
- 训练了GAN-LSTM模型来预测循环压力,透率 (ROP),井头压力和手指重量.
主要成果:
- 该GAN-LSTM模型展示了对循环压力,井头压力,ROP和总重量的预测能力.
- 在经过广泛的训练后,实现了大约90%的预测准确度.
- 与独立的GAN和LSTM模型相比,精度提高了17%.
结论:
- 开发的GAN-LSTM模型为卷轴管道操作提供了重要的指导价值.
- 该模型有助于提高操作安全性和提高钻井效率.
- 预测准确度支持降低石油和天然气生产成本.
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