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油田注水管道内腐蚀剩余寿命预测研究

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埋地管道的寿命直接影响着油气管道公司的经济效益，准确预测埋地管道腐蚀剩余寿命能够提前制定维修计划，减少经济损失。为估算管线剩余安全服役年限，创建了基于主成分分析(PCA)和粒子群(PSO)结合蚁群(ACO)的混合连续优化算法(HCACO)的最小二乘支持向量机(LSSVM)预测模型。首先，通过PCA降维提取管道腐蚀的主要影响因素，以优化预测模型的输入变量。其次，采用HCACO对LSSVM中的惩罚因子C和核函数参数σ2进行寻优，并将优化后的参数代入LSSVM预测模型中，最终构建基于PCA-HCACO-LSSVM的腐蚀管道剩余寿命预测模型。以某油田注水管道为例，并与另外三种模型BP、SVM以及当前较流行的GRA-XGBoost进行对比，结果PCA-HCACO-LSSVM模型中均方根误差(RMSE)为0。303，平均绝对误差(MAE)为0。175，拟合优度(R2)为0。993，模型评估指标值均优于其余三种模型，预测精度及泛化性能也得到了显著提高，剩余寿命预测值与实际值更接近，为石油天然气管线的防腐研究开辟了新途径。
Research on Prediction of Remaining Life of Internal Corrosion of Oilfield Water Injection Pipelines
The life of buried pipelines directly affects the economic benefits of oil and gas pipeline companies,and accurate prediction of the remaining life of buried pipeline corrosion can make maintenance plans in advance and reduce economic losses.In order to estimate the remaining safe service life of the pipeline,a Least Squares Support Vector Machine(LSSVM)prediction model based on Principal Component Analysis(PCA)and Particle Swarm(PSO)combined with Ant Colony(ACO)Hybrid Continuous Optimization Algorithm(HCACO)was created.Firstly,the main influencing factors of pipeline corrosion were extracted by PCA dimensionality reduction to optimize the input variables of the prediction model.Secondly,HCACO was used to optimize the penalty factor and kernel function pa-rameters in LSSVM,and the optimized parameters were substituted into the LSSVM prediction model.Finally,the remaining life prediction model of corroded pipelines based on PCA-HCACO-LSSVM was constructed.Taking the water injection pipeline of an oilfield as an example,and comparing it with the other three models BP,SVM and the current popular GRA-XGBoost,it is concluded that the root mean square error(RMSE),mean absolute error(MAE)and goodness-of-fit(R2)of the PCA-HCACO-LSSVM model were 0.303,0.175 and 0.993.The evaluation index values of the model are better than those of the other three models,the prediction ac-curacy and generalization performance have also been significantly improved,and the predicted value of remaining life is closer to the actual value,which opens up a new way for the anti-corrosion research of oil and gas pipelines.

oilfield water injection pipelinesinternal corrosionremaining life predictionprincipal component analysis(PCA)hybrid continuous ant colony optimization(HCACO)least squares support vector machine(LSSVM)

骆正山、杜丹

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西安建筑科技大学管理学院,陕西西安 710055

油田注水管道 内腐蚀 剩余寿命预测 主成分分析法(PCA) 混合连续优化算法(HCACO) 最小二乘支持向量机(LSSVM)

2024

10.20165/j.cnki.ISSN1673-629X.2024.0278

计算机技术与发展
陕西省计算机学会

计算机技术与发展

CSTPCD
影响因子：0.621
ISSN：1673-629X
年,卷(期)：2024.34(12)