Strain Extremum Characterization Method for Oil and Gas Pipelines under Oblique Reverse Faults

doi:10.3969/j.issn.1004-132X.2025.11.039

Abstract

Abstract:

Long-distance pipelines were more susceptible to significant deformation under the influences of oblique reverse faults， and their strain extremums were difficult to accurately characterize， making it challenging to reflect the actual deformation states of the pipelines. To address this issue， a strain extremum characterization method for pipelines was proposed based on strain sensor monitoring to enhance the accuracy and early warning capability of buried long-distance pipeline strain monitoring. A pipe-soil coupling finite element model was established to analyze the deformation behavior of pipelines under oblique reverse faults， and the accuracy of the model was validated. Then， a high-precision tensile and compressive strain extremum characterization model was constructed using Stacking ensemble learning. Finally， a pipeline strain dataset was established， the strain data from the 3， 9 and 12 o' clock positions along the pipelines within 20 times the pipe diameter on both sides the fault was utilized to train the model. This model accurately fits the strain characteristics of the pipelines under multidimensional nonlinear conditions， effectively capturing stress concentration and deformation patterns induced by oblique reverse faults. The results show that the proposed strain extremum characterization method may precisely reflect the maximum tensile and compressive strains of pipelines under oblique reverse faults， providing significant engineering value for pipeline deformation monitoring and early warning systems.

Key words: oblique reverse fault, oil and gas pipeline, large strain, monitoring, finite element simulation

摘要：

长输管道在斜逆断层作用下更易发生较大变形，并且其应变极值难以精确表征，无法准确反映管道真实变形情况。针对这一问题，提出了一种基于应变传感器监测的管道应变极值表征方法，以提高埋地长输管道应变监测的精度和预警能力。构建了管-土耦合有限元模型，分析斜逆断层作用下管道的变形规律，并对模型的准确性进行验证；基于Stacking集成学习构建了高精度的拉压应变极值表征模型；最后建立管道应变数据集，将管道3、9、12点钟方向沿断层两侧各20倍管径的应变数据用于训练模型，该模型能够在多维非线性条件下精确拟合管道的应变特性，有效捕捉斜逆断层作用下管道的应力集中与变形模式。研究结果表明，所提出的应变极值表征方法能准确反映斜逆断层作用下管道的最大拉压应变，对管道变形监测与预警具有重要的工程应用价值。

关键词: 斜逆断层, 油气管道, 大应变, 监测, 有限元仿真

CLC Number:

TE88

Xiangchong GAO, Peijun LIU, Huanguo CHEN, Chengcheng PENG, Shujia SONG, Zijun LIN. Strain Extremum Characterization Method for Oil and Gas Pipelines under Oblique Reverse Faults[J]. China Mechanical Engineering, 2025, 36(11): 2801-2809.

高祥冲, 刘培君, 陈换过, 彭程程, 宋树家, 林子竣. 斜逆断层作用下的油气管道应变极值表征方法[J]. 中国机械工程, 2025, 36(11): 2801-2809.

Figures/Tables 15

Fig.1 Sensor locations， fault extremum points， and failure modes

Fig.2 Strain-based early warning method

Fig.3 Pipe-soil coupling model

Fig.4 Ramberg-Osgood model constitutive relation for X80 pipeline

Tab.1 Soil physical properties

密度/

（kg·m^-3）

剪胀角/

（°）

压缩模量/

MPa

黏聚力/

kPa

摩擦角/

（°）

泊松比

1950

0.4

Fig.5 Schematic diagram of strike-slip fault test device and strain sensor layout

Fig.6 Sensor installation and backfill soil

Fig.7 Strain data verification at positions of 0.75， 1.00， and 1.50 m

Fig.8 Pipeline strain response under fault displacement of 3 m

Fig.9 Variation of monitoring strain at 3， 9， and 12 o' clock positions with fault displacement

Fig.10 Strain calculation by using the three-point monitoring value formula

Fig.11 Basic framework of Stacking ensemble learning

Fig.12 Strain characterization flowchart based on Stacking ensemble learning

Tab.2 Performance metrics comparison table

模型	RMSE	MAE	R²
DNN	0.0009	0.0189	0.8536
MLP	0.0005	0.0078	0.9299
XGBoost	0.0007	0.0157	0.8723
Stacking 集成模型	0.0002	0.0035	0.9899

Fig.13 Prediction of pipeline strain extremum based on Stacking model

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