基于深度森林算法的油井产量预测研究

为了克服传统机器学习算法产量预测模型的缺点，以深度森林算法理论为基础，综合油井相关各项数据，建立了油井产量预测新模型。首先应用KNN最邻近方法和Z-Score标准化方法对油井相关数据进行预处理，利用MDI特征选择方法选择对油井产量影响最大的特征向量，然后将选出的特征向量作为深度森林模型的输入变量，建立深度森林产量预测模型，利用网格化搜索优化模型参数，最后在测试集上运行模型，对模型性能进行评估。研究结果表明，相对于BP神经网络等传统机器学习算法模型，深度森林模型的产量预测精度更高，可以准确预测油井产量，同时相对于深度神经网络等复杂学习算法，该算法参数少、调参及应用简单，为油井产量预测提供了一种新的方法和思路。

Study on Production Prediction of Oil Well Based on Deep Forest

In order to overcome the shortcomings of the traditional machine learning production prediction model, a new oil well production prediction model based on the theory of deep forest algorithm is established. Firstly, using the KNN nearest neighbor method and the Z-Score standardized method to process the oil well data, use the MDI feature selection method to select the feature factors that have the greatest impact on oil well production, and then use the selected features as input variables of the deep forest model to establish a deep forest production prediction model, then use grid search to optimize model parameters, finally run the model on the test set to evaluate model performance. The research results show that the deep forest model has smaller production prediction errors and higher accuracy than traditional machine learning algorithm models, such as BP neural network model. Deep forest production prediction model can accurately predict oil well production, simultaneously, compared with complex learning algorithms such as deep neural network, the algorithm has fewer parameters, which make it simple for application, and provide a new method for oil well production prediction.