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Updated: Jan 15, 2026

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

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在小型样本场景中,转移学习驱动的石油和天然气管道腐蚀率的预测

Pujun Long¹, Guopan Yang¹, Haiyang Liu¹

¹School of Mathematical and Physical Sciences, Chongqing University of Science and Technology, Chongqing, China.

|October 16, 2025

概括

这项研究引入了一种新的CNN-LSTM-变压器模型,用于预测天然气井管道腐蚀率. 带有分阶段微调的转移学习方法显著提高了小样本场景的准确性.

科学领域:

石油工程是石油工程中的一个.
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

石油和天然气管道的腐蚀带来了重大的安全和经济风险.
准确的腐蚀率预测对于在复杂环境中保持管道完整性至关重要.
现有的方法在腐蚀预测中常见的小样本数据集中扎.

研究的目的:

开发一种先进的转移学习模型,用于准确预测天然气井管道中的腐蚀速率.
为了应对腐蚀分析中数据可用性有限的挑战.
提高油气管道系统的安全性和运营效率.

主要方法:

开发了一个混合深度学习模型,集成卷积神经网络 (CNN),长短期记忆 (LSTM) 网络和变压器模块.
美国有线电视新闻网提取了局部特征,LSTM捕获了时间依赖性,而变压器使用多头注意力解决了远程依赖性.
一个分阶段的微调策略促进了知识从来源转移到目标领域,以提高适应能力.

主要成果:

拟议的CNN-LSTM-变压器模型,经过分阶段的微调,实现了高预测准确度.
关键性能指标包括0.021的平均绝对误差 (MAE),0.031的根平均平方误差 (RMSE) 和0.909.90的R平方 (R2).
该模型在腐蚀率预测方面明显优于其他转移学习方法.

结论:

开发的转移学习模型为预测管道腐蚀率提供了强大的解决方案,特别是在有限的数据条件下.
混合CNN-LSTM-变压器架构有效利用各种数据特性和依赖性.
这种方法提高了对石油和天然气基础设施的安全评估和维护策略的可靠性.

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