摘要：Lost circulation, a common risk during the drilling process, significantly impacts drilling safety and efficiency. The presence of data noise and temporal evolution characteristics pose significant challenges to the accurate monitoring of lost circulation. Traditional supervised intelligent monitoring methods rely on large amounts of labeled data, which often do not consider temporal fluctuations in data, leading to insufficient accuracy and transferability. To address these issues, this paper proposes an unsupervised time series autoencoder (BiLSTM-AE) intelligent monitoring model for lost circulation, aiming to overcome the limitations of supervised algorithms. The BiLSTM-AE model employs BiLSTM for both the encoder and decoder, enabling it to comprehensively capture the temporal features and dynamic changes in the data. It learns the patterns of normal data sequences, thereby automatically identifying anomalous risk data points that deviate from the normal patterns during testing. Results show that the proposed model can efficiently identify and monitor lost circulation risks, achieving an accuracy of 92.51%, a missed alarm rate of 6.87%, and a false alarm rate of 7.71% on the test set. Compared to other models, the BiLSTM-AE model has higher accuracy and better timeliness, which is of great significance for improving drilling efficiency and ensuring drilling safety.

关键字：lost circulation monitoring; unsupervised; time series autoencoder; BiLSTM-AE

ISSN号：2227-9717

卷、期、页：卷12期7

发表日期：2024-07-01

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：PROCESSES

参与作者：Wu, Liwei,Wang, Xiaopeng,Zhu, Guowei,Zhang, Qilong,Dong, Pinghua,Wang, Jiangtao

通讯作者：张子悦

第一作者：祝兆鹏

论文类型：期刊论文

论文概要：Wu, Liwei,Wang, Xiaopeng,张子悦,Zhu, Guowei,Zhang, Qilong,Dong, Pinghua,Wang, Jiangtao,祝兆鹏，Intelligent Monitoring Model for Lost Circulation Based on Unsupervised Time Series Autoencoder，PROCESSES，2024，卷12期7

论文题目：Intelligent Monitoring Model for Lost Circulation Based on Unsupervised Time Series Autoencoder