在UGS中快速计算底孔压力的理论和数据驱动方法
Yang Li1, Haiwei Guo2, Xianfeng Gong3
1Zhongyuan Oilfield Informatization Management Center Department, Sinopec, Puyang, 457001, China. yli20220727@163.com.
Scientific reports
|March 15, 2025
概括
一个新的理论和数据驱动的神经网络模型 (TDDNN) 准确地计算了地下气体储存 (UGS) 中的底孔压力. 这种方法提高了效率和精度,为类似的数据有限的深度学习应用提供了有价值的方法.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能在能源中的作用
- 储水库工程 储水库工程
背景情况:
- 精确的底孔压力计算对于地下气储 (UGS) 操作至关重要,影响动态分析和生产优化.
- 传统方法在效率和精度方面面临挑战,特别是在动态的UGS环境中,包括注入,提取和关闭阶段.
研究的目的:
- 开发一种创新和高效的方法来计算UGS操作中的底孔压力.
- 通过改进压力计算,提高UGS的运行和维护效率.
主要方法:
- 对影响底孔压力因素的综合分析,基于井口流量理论.
- 使用与底孔压力相关的特征变量构建神经网络模型.
- 将井口流量方程与理论样本和现实数据集成为理论和数据驱动的神经网络模型 (TDDNN).
主要成果:
- 开发的TDDNN模型实现了快速而准确的底孔压力计算.
- 这种新方法在关键精度指标 (MAE,MSE,RMSE,MAPE,R2) 上表现优于传统技术.
- 与传统的理论方法相比,计算时间从秒到毫秒显著减少.
结论:
- TDDNN方法为UGS的底孔压力计算提供了显著的进步.
- 这种方法提供了高预测准确度和增强的计算效率,这对于UGS管理至关重要.
- 该研究为在能源部门的样本有限环境中应用深度学习提供了有价值的参考.
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