基于CNN-GRU-LightGBM模型的单井产量 预测方法
DOI:
作者:
作者单位:

东北石油大学电气信息工程学院

作者简介:

通讯作者:

中图分类号:

TE341

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Single well production forecasting method based on the CNN-GRU-LightGBM model
Author:
Affiliation:

School of Electrical Information Engineering,Northeast Petroleum University

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    单井日产量趋势预测研究在油田生产中具有重要意义。由于油井生产工况复杂,难以准确预测日产量,本文建立了基于多变量时序数据的产量模型。基于卷积门控循环单元(Convolutional Neural Network – Gate Recurrent Unit, CNN-GRU)提取深层特征进行时序预测,基于梯度提升框架的决策树模型(Light Gradient Boosting Machine, LightGBM)从回归预测角度进行预测,两者结果相互融合,进一步提高产量预测精度。同时,本文提出了一种可以实现多变量时序预测或回归预测模型在未知输入特征情况下准确预测产量的方法—超前参数递归预测策略。采用该方法对影响产量的重要特征进行超前预测,并将预测到的重要特征应用于预测产量的仿真测试中。仿真结果表明:本文所建立模型与超前参数递归策略配合最好,在测试集上的预测准确度最高。相比单变量时序预测和回归预测模型,可显著提高预测精度。

    Abstract:

    A study on prediction for daily production per well is of great significance in oilfield production. Under complex production conditions of oil wells, it is difficult to accurately predict daily production. A production model based on multi-variable time series data is built in this paper. Based on the convolutional neural networks - gate recurrent unit (CNN-GRU), deep features are extracted for timing prediction, and a decision tree model (LightGBM) based on the gradient boosting framework provides prediction results from the perspective of regression prediction. The results of the two are integrated with each other to further improve the accuracy of production prediction. A method that supports multi-variable time series prediction or regression prediction to accurately predict production under unknown input characteristics – strategy for recursive prediction of advance parameters is proposed. This method is used to predict important features that affect production in advance, and the predicted important features are used in simulation tests on production prediction. The simulation results show that the model established in this paper works best with the advance parameter recursive strategy and has the maximum prediction accuracy on the test set. Compared with univariate time series prediction and regression prediction models, the prediction accuracy can be significantly improved.

    参考文献
    相似文献
    引证文献
引用本文

杨莉,周子希,王婷婷,等. 基于CNN-GRU-LightGBM模型的单井产量 预测方法[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-08-02
  • 最后修改日期:2023-11-11
  • 录用日期:2023-11-13
  • 在线发布日期:
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注