基于CNN-GRU-LightGBM模型的单井产量预测方法  

Single Well Production Forecasting Method Based on CNN-GRU-LightGBM Model

作　　者：杨莉[1] 周子希 王婷婷[1] 王艳铠 YANG Li;ZHOU Zi-xi;WANG Ting-ting;WANG Yan-kai(School of Electrical&Information Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,大庆163318

出　　处：《科学技术与工程》2024年第18期7606-7614,共9页Science Technology and Engineering

基　　金：国家自然科学基金(52074088);黑龙江省博士后科研启动项目(LBH-Q21086);东北石油大学电气青年拔尖人才基金(DYDQQB202206);东北石油大学省杰青后备人才项目(SJQH202002);黑龙江省省属高校基本科研业务费:控制科学与工程团队专项项目(2022TSTD-04)。

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,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集成模型(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,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。Accurately predicting daily production trends for individual wells in oilfield operations is crucial,but it poses a significant challenge due to the complex nature of oil well production conditions.A production model was developed based on multivariate time series data.Deep features were extracted using CNN-GRU(convolutional neural network-gate recurrent unit)for time series prediction,and predictions were also made using LightGBM(light gradient boosting machine)framework from a regression perspective.To further enhance production prediction accuracy,the results of both approaches were integrated.Additionally,a method called the advanced parameter recursive prediction strategy was proposed,which allows for accurate production prediction even without known input features.This strategy involves forecasting important features that affect production in advance and applying these predicted features to simulate production prediction tests.The simulation results demonstrate that the model established in this study,combined with the advanced parameter recursive strategy,achieves the highest prediction accuracy on the test set.It significantly improves prediction accuracy compared to single-variable time series prediction and regression prediction models.

关 键 词：单井产量预测 超前参数预测 CNN-GRU LightGBM 

分 类 号：TE349[石油与天然气工程—油气田开发工程]