基于多模型融合Stacking集成学习的油田产量预测

基于机器学习前沿理论，提出一种基于多模型融合Stacking集成学习方式的组合预测方法，以国内某特高含水油田区块中多口水驱产油井历年生产历史数据为试验样本，预测其动态产油量。依据不同算法的训练原理，选取极限梯度提升树算法、长短记忆网络（LSTM）、时域卷积网络（TCN）等作为模型的基学习器，采用多元线性回归作为模型的元学习器。结果表明：融合后的Stacking模型充分发挥了各基学习器的优势，相比单一模型，融合后的Stacking模型预测平均误差较小，预测鲁棒性较好。该模型的提出对融合模型在特高含水油藏开发方面具有重要的应用意义。

Production Prediction of Oilfield Based on Multi-model by Stacking Ensemble Learning

An oil production prediction method based on multi-model combination under Stacking ensemble learning was proposed associated with the frontier theory of machine learning. The model was used to predict the dynamic oil production from the production data of a domestic ultra-high permeability oilfield in China developed by water flooding. Considering the differences in training principles of different algorithms, the XGBoost algorithm, long and short memory network (LSTM), temporal convolutional network (TCN) and other models are selected as base learners, the MLR algorithm is chosen as meta learner. The results show that the Stacking ensemble model has smaller average error and better prediction robustness compared with the traditional single model, since the ensemble model fully combined the advantages of each base learner. The proposed model is of great significance to the application in ultra-high water cut reservoirs.