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.