Research on Prediction Method of Casing Damage Based on Stacking
According to the characteristics of many factors affecting casing damage and complex data in the oil and gas produc-tion process,the field data is analyzed and integrated through data preprocessing,random forest importance analysis and other tech-nologies,and the method of feature engineering is used to process missing values and select feature parameters.Aiming at the prob-lem that traditional machine learning models are not good at predicting the set loss,a two-layer stacking mode ensemble learning prediction model is proposed.The model uses random forest,support vector machine,gradient boosting decision tree and K-nearest neighbor algorithm as the base model,and logistic regression as the meta-model to build a more generalized set loss prediction mod-el.The results show that the model has improved accuracy and F1 value compared with a single machine learning model,and the fi-nal accuracy of the model reaches 89.21%.
ensemble learningcasing damagecasing damage predictionStacking model fusion
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