采油过程多尺度状态特征生成的有杆泵动态液面预测

侯延彬; 高宪文; 李翔宇

doi:10.11949/0438-1157.20190352

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采油过程多尺度状态特征生成的有杆泵动态液面预测

过程系统工程 | 更新时间：2020-06-24

- 采油过程多尺度状态特征生成的有杆泵动态液面预测
- Prediction for dynamic liquid level of sucker rod pumping using generation of multi-scale state characteristics in oil field production
- 化工学报 2019年70卷第S2期页码：311-321
- 作者机构：
  
  1.东北大学信息科学与工程学院，辽宁沈阳 110819
  2.沈阳理工大学自动化与电气工程学院，辽宁沈阳 110168
- 作者简介：
  
  侯延彬（1984—），男，博士研究生，houyanbin1984@gmail.com
  高宪文（1954—），男，博士，教授，gaoxianwen@ise.neu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61573088)
- DOI：10.11949/0438-1157.20190352
  中图分类号： TP 29
- 收稿：2019-04-03，
  
  修回：2019-06-14，
  
  纸质出版：2019-09-05
- 稿件说明：
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侯延彬, 高宪文, 李翔宇. 采油过程多尺度状态特征生成的有杆泵动态液面预测[J]. 化工学报, 2019, 70(S2): 311-321

HOU Yanbin, GAO Xianwen, LI Xiangyu. Prediction for dynamic liquid level of sucker rod pumping using generation of multi-scale state characteristics in oil field production[J]. CIESC Journal, 2019, 70(S2): 311-321
侯延彬, 高宪文, 李翔宇. 采油过程多尺度状态特征生成的有杆泵动态液面预测[J]. 化工学报, 2019, 70(S2): 311-321 DOI： 10.11949/0438-1157.20190352.

HOU Yanbin, GAO Xianwen, LI Xiangyu. Prediction for dynamic liquid level of sucker rod pumping using generation of multi-scale state characteristics in oil field production[J]. CIESC Journal, 2019, 70(S2): 311-321 DOI： 10.11949/0438-1157.20190352.

摘要

动态液面是反映油藏供液能力的重要参数，生产现场多采用回声仪进行检测，其检测效率低，无法连续检测，存在安全隐患。针对现有基于数据驱动的预测方法由于各种原因造成历史数据不足，进而导致建模困难的问题，通过对机理进行分析，找出与动态液面关系密切的多尺度状态特征；采用生成对抗网络生成状态特征，解决历史数据不足的问题；仿真实验表明生成的数据可以用于建立动态液面预测模型，并确定采用支持向量回归作为建模方法；最后在油田生产现场使用本文提出的建模方法，实际预测结果表明本方法的有效性，满足油田工程应用要求。

Abstract

Dynamic liquid level is an important parameter to reflect the liquid supply capacity of oil deposit. In the actual production site

dynamic liquid level is usually detected by the echo meter. This method is limited by the low detection efficiency

discrete measurement

and safety hazards. In addition to this

the existing prediction methods based on data-driven have difficulties in modeling due to the lack of historical data caused by various reasons. In response to the problem about lack of historical data

the multi-scale state characteristics which are closely connected with dynamic liquid level by analyzing the mechanism has been found out in this paper. Further

the state characteristics have been generated by using generative adversarial network (GAN). Simulation experiments have shown that the generated data can be used to establish dynamic liquid level prediction model and solve the problem about lack of historical data. On this basis

support vector regression (SVR) has been adopted as the modeling method and used in oil field production. Finally

the actual prediction results show that the proposed method is effective and meets the application requirements of oilfield engineering.

关键词

Keywords

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