钻井液与完井液2025，Vol.42Issue(6)：772-780,9.DOI:10.12358/j.issn.1001-5620.2025.06.009
基于长短期记忆网络和随机森林的井漏预测
Lost Circulation Prediction Based on Long Short-Term Memory Network and Random Forest Algorithm
摘要
Abstract
Lost circulation is one of the key factors restricting drilling safety and efficiency.To realize accurate prediction of lost circulation,a hybrid model for the prediction of lost circulation is presented based on long short-term memory(LSTM)and random forest(RF)algorithm.The LSTM model,the RF model and the LSTM-RF hybrid model are constructed based on algorithm principle.Fourteen lost circulation characteristic parameters are selected using correlation analysis method,and are input into three lost circulation prediction models for training.The performance and lost circulation prediction accuracy of the three models are then analyzed and compared.The experimental results show that the root mean square error(RMSE)of the hybrid model on the test dataset is 0.11,the mean absolute error(MAE)is 0.22,the coefficient of determination(R2)is 0.95,and the overall accuracy reaches 84.2%,each indicator is better than that of the single model.Furthermore,hybrid model has successfully predicted 5 times of lost circulation in field application.The results of this study show that LSTM-RF hybrid model is a model with optimal comprehensive performance in lost circulation prediction,it can predict lost circulation more precisely,and can provide reference for the prevention of lost circulation and for the decision making in drilling operation.关键词
井漏预测/特征参数/长短期记忆网络/随机森林/混合模型Key words
Lost circulation prediction/Characteristic parameter/Long short-term memory network/Random forest/Hybrid model分类
能源科技引用本文复制引用
蔡艾廷,苏俊霖,戴昆,赵晗,王嘉义..基于长短期记忆网络和随机森林的井漏预测[J].钻井液与完井液,2025,42(6):772-780,9.基金项目
中国石油-西南石油大学创新联合体科技合作项目"降低长水平段井下复杂与事故的配套技术"(2020CX040201). （2020CX040201）
