KGPM:一种以知识为导向的预测模型,用于天然气井的虚拟计量.
Jinman Li1, Yang Lin1, Chuangfeng An1
1Tianjin Branch of CNOOC (China) Co., Ltd., Tianjin 300452, China.
ACS omega
|January 6, 2025
概括
本研究介绍了一个以知识为导向的预测模型 (KGPM),以改进智能油气田的虚拟流量计 (VFM). 通过整合先前的知识,KGPM增强了数据驱动模型,从而实现更可靠的实时生产预测.
科学领域:
- 石油工程是石油工程中的一个.
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 有效的数据利用对于智能油气田开发至关重要.
- 虚拟流量计 (VFM) 提供实时生产数据,没有额外的传感器,使用压力和温度数据.
- 当前数据驱动的VFM模型在解释性和输出可靠性方面扎.
研究的目的:
- 为了提高数据驱动的虚拟流量计 (VFM) 模型的可靠性.
- 提出一种混合模型,将先前的知识整合到数据驱动的方法中.
- 为VFM应用开发一个以知识为导向的预测模型 (KGPM).
主要方法:
- 开发了一种混合模型,将先前的知识作为限制纳入长期短期记忆 (LSTM) 神经网络.
- 使用此先前知识进行指导数据驱动模型培训.
- 专门为VFM建立了一个以知识为导向的预测模型 (KGPM).
主要成果:
- 该KGPM在流速预测方面表现出了卓越的熟练程度.
- 一个实验井的平均绝对百分比误差 (MAPE) 为3.211%.
- 在第二个实验井中获得了1.141%的MAPE.
结论:
- 拟议的KGPM显著提高了VFM的可靠性.
- 这项研究有助于优化用于智能油气田的VFM技术.
- 该KGPM提供了一个更可解释和可靠的替代纯粹数据驱动的VFM模型.
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