Soft sensor modeling for dynamic liquid level of oil well based on fuzzy inference adaptive updating

WANG Tong; DUAN Zewen

doi:10.11949/0438-1157.20190729

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Soft sensor modeling for dynamic liquid level of oil well based on fuzzy inference adaptive updating

Process system engineering | 更新时间：2026-07-24

- Soft sensor modeling for dynamic liquid level of oil well based on fuzzy inference adaptive updating
- CIESC Journal Vol. 70, Issue 12, Pages: 4760-4769(2019)
- 作者机构：
  
  沈阳工业大学电气工程学院，辽宁沈阳 110870
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20190729
  CLC： TQ 063
- Received：27 June 2019，
  
  Revised：2019-08-09，
  
  Online First：09 June 2020，
  
  Published：05 December 2019
- 稿件说明：
移动端阅览
王通, 段泽文. 基于模糊评估自适应更新的油井动液面软测量建模[J]. 化工学报, 2019, 70(12): 4760-4769

WANG Tong, DUAN Zewen. Soft sensor modeling for dynamic liquid level of oil well based on fuzzy inference adaptive updating[J]. CIESC Journal, 2019, 70(12): 4760-4769
王通, 段泽文. 基于模糊评估自适应更新的油井动液面软测量建模[J]. 化工学报, 2019, 70(12): 4760-4769 DOI： 10.11949/0438-1157.20190729.

WANG Tong, DUAN Zewen. Soft sensor modeling for dynamic liquid level of oil well based on fuzzy inference adaptive updating[J]. CIESC Journal, 2019, 70(12): 4760-4769 DOI： 10.11949/0438-1157.20190729.

摘要

针对常规动液面软测量方法在面对复杂、多变的工况时缺乏有效的模型更新机制、预测精度不足等问题，提出了一种基于模糊评估的自适应更新建模策略，通过基于模糊推理产液量变化趋势拟合的模型性能评价模块，动态更新模型，实现对原测量模型的反向推理验证。首先离线建立不同工况的动液面多模型预测集，然后根据产液量拟合优度指标对动液面在线输出模型进行实时的输出评估判断，利用相似样本数据进行模型的在线更新，使其能不断适应油井的工况变化，自适应获得更加准确的软测量模型。最后通过辽河油田现场生产数据验证表明，该方法能够有效提高模型的预测精度和泛化能力，可以满足油田现场的生产需求。

Abstract

For the complex and changing operation conditions

the traditional soft sensor methods for the dynamic liquid level lack effective model updating mechanism and have poor prediction accuracy. To solve this problem

an adaptive model updating strategy based on the fuzzy evaluation is proposed in this paper. A model performance evaluation module based on the fuzzy inference for the tendency of the change of the oil liquid is constructed to dynamically update the model

which is used to realize the reverse inference verification for the original model. Firstly

an offline multi-model prediction model for the dynamic liquid level was established; Then

a fitting optimization index of the oil liquid was put forward to do real-time output evaluation for the online output of the dynamic liquid level; Finally

the field production data verification in Liaohe Oilfield shows that the method can effectively improve the prediction accuracy and generalization ability of the model

and can meet the production needs of the oilfield site.

关键词

Keywords

references

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