Research on Prediction Method of Casing Damage Based on Stacking
ZHAO Jianmin
ZHANG Junbo
CUI Jiaxin
Abstract：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%.
Keywords：ensemble learningcasing damagecasing damage predictionStacking model fusion
Publication Date：2024-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1685-1690 )