基于机器学习和Shapley增量解释的丘陵地形油气管道液体保持的可解释预测模型
Xinhui Chen1,2, Si Huang1,2, Tianri Guan3
1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
ACS omega
|April 28, 2025
概括
机器学习准确地预测了山坡油气管道中的液体阻滞,提高了流量保证. 像XGBoost和SHAP这样的可解释模型解释了预测,解决了流量保证研究中的差距.
科学领域:
- 石油工程是石油工程中的一个.
- 流量保证 流量保证
- 机器学习应用 机器学习应用
背景情况:
- 在丘陵地形的油气管道中,液体加载对流量保证产生重大影响.
- 传统的机械模型与液体装载和管道运行之间的非线性关系作斗争.
- 现有的机器学习研究往往缺乏用于液体负载预测的解释性.
研究的目的:
- 开发一种可解释的预测模型,用于油气管道中的液体保留.
- 将机器学习 (ML) 与Shapley增量解释 (SHAP) 整合起来,以增强模型理解.
- 解决传统模型的局限性,改善复杂地形的流量保证.
主要方法:
- 使用OLGA软件模拟了山地地形油气管道的运行条件.
- 训练并比较六个ML模型用于液体持仓预测,通过四个指标评估性能.
- 使用SHAP对表现最好的XGBoost模型进行全球和本地解释.
主要成果:
- 极端梯度增强 (XGBoost) 实现了最高的预测性能 (R2=0.986,MAE=0.012,MAPE=0.030,RMSE=0.016). 在此过程中,XGBoost实现了最高的预测性能 (R2=0.986,MAE=0.012,MAPE=0.030,RMSE=0.016).
- SHAP分析提供了关于特征对平均流动性持有量的贡献的见解.
- 在各种操作条件下,确定了影响流动性持有量的特征相互作用的趋势.
结论:
- 可解释的XGBoost-SHAP模型有效地预测了流动性持有量,并增强了对影响因素的理解.
- 这种方法在具有挑战性的石油和天然气管道运营中为流量保证提供了显著的进步.
- 这些发现有助于更好地管理液体装载和优化管道性能.
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