摘要：Stuck pipe phenomena can have disastrous effects on drilling performance, with outcomes that can range from time delays to loss of expensive machinery. In this work, we provide three methods for the prediction of stuck pipe. The first method targets the detection of friction coefficient which can represent the trend of stuck pipe. The second method targets the prediction of probability for stuck pipe using ANN (artificial neural network). The last model establishes a comprehensive indicator based on the first and the second method using fuzzy mathematics which can give more accurate probability for stuck pipe. The results show that the best model is the last one which can predict stuck pipe events with a F1 of 0.98 and a FAR (false alarm rate) of 1%. Preliminary experimental results on the available dataset indicate that the use of the proposed model and can help mitigate the stuck pipe issue.

关键字：stuck pipe; drag coefficient; neural network; fuzzy mathematics

ISSN号：2076-3417

卷、期、页：卷: 12期: 10

发表日期：2022-05-01

影响因子：2.679000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,SCIE（科学引文索引网络版）

发表期刊名称：APPLIED SCIENCES-BASEL

参与作者：朱硕

通讯作者：姚学喆,刘慕臣

第一作者：宋先知,祝兆鹏

论文类型：期刊论文

论文概要：朱硕,宋先知,祝兆鹏,姚学喆,刘慕臣，Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model，APPLIED SCIENCES-BASEL，2022，卷: 12期: 10

论文题目：Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model