Prediction on casing life of thermal production wells based on IA-SVM model

Luo Zhengshan; Bi Aorui

doi:10.13809/j.cnki.cn32-1825/te.2018.01.012

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Prediction on casing life of thermal production wells based on IA-SVM model

更新时间：2025-12-05

- Prediction on casing life of thermal production wells based on IA-SVM model
- Petroleum Reservoir Evaluation and Development Issue 1, Pages: 58-63(2018)
- 作者机构：
  
  西安建筑科技大学管理学院
- 作者简介：
- 基金信息：
- DOI：10.13809/j.cnki.cn32-1825/te.2018.01.012
  CLC： TE931.2
- Published：2018
- 稿件说明：
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[1]骆正山,毕傲睿.基于IA-SVM模型的热采井套管寿命预测[J].油气藏评价与开发,2018,8(01):58-63.

Luo Zhengshan, Bi Aorui. Prediction on casing life of thermal production wells based on IA-SVM model[J]. Petroleum Reservoir Evaluation and Development, 2018, (1): 58-63.
[1]骆正山,毕傲睿.基于IA-SVM模型的热采井套管寿命预测[J].油气藏评价与开发,2018,8(01):58-63. DOI： 10.13809/j.cnki.cn32-1825/te.2018.01.012.

Luo Zhengshan, Bi Aorui. Prediction on casing life of thermal production wells based on IA-SVM model[J]. Petroleum Reservoir Evaluation and Development, 2018, (1): 58-63. DOI： 10.13809/j.cnki.cn32-1825/te.2018.01.012.

摘要

为解决稠油开采过程中热采井套管因高温高压、泥沙磨损等原因导致变形、破损以及泄漏的问题

提出一种基于免疫算法（IA）优化支持向量机（SVM）的套管寿命预测模型。首先

应用免疫算法得到影响支持向量机预测精度的三个参数的精确值;其次

将5个影响套管寿命的因素作为支持向量机的输入变量

套管寿命作为目标输出

建立寿命预测模型;最后以辽河油田运行的套管为例

验证模型预测热采井套管寿命的效果。结果表明

该模型能够很好地预测热采井套管的寿命

误差小、吻合度高

为套管的维护和维修提供了参考依据。

Abstract

In order to solve the problems of the distortion

damage and leakage of the casing pipes caused by the high temperature

high pressure

and the sand erosion of the thermal production wells during the exploitation of the heavy oil

a life prediction model based on the immune algorithm（IA）and the support vector machine（SVM）was built. Firstly

the IA was used to obtain the exact values of three parameters that affected the prediction accuracy of the SVM. Secondly

the life prediction model was built by taking 5 factors that affected the life of the casing as the input variables of the SVM

and the casing life as the target output. Finally

the casing wells running in Liaohe oilfield were taken as the examples to test the performance of the model. The results showed that the model could predict the casing life well with small errors and high coincidence. It provides a reference for the maintenance and repair of the casing.

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