关键词: 随机森林, 融合模型, 压井方法分类判断, 井口压力控制

Abstract: In wellhead pressure control operations, the traditional method relies too much on expert experience and the accuracy of mathematical model calculations. In this paper, we propose a multi-model fusion algorithm based on Random Forest (RF) for judgments of classification of well killing methods. Firstly, the structure and data of expert experience are transformed into a data form that can be used by machine learning models. Meanwhile, the basic data of oil and gas wells and working condition parameters are used as important parameters of intelligent model to describe the feature space of well killing operations. Then, the feature data are processed by feature engineering for feature selection, feature code and feature choose. Finally, a stacking double-layer fusion model based on Random Forest is constructed for the implement of predictions for classification of well killing method. The experimental results show that our method has more high prediction accuracy than other machine learning algorithms that has only single model.

Key words: Random Forest, fusion model, well killing method judgment of classification, wellhead pressure control