摘要：Correct detection of drilling abnormal incidents while minimizing false alarms is a crucial measure to decrease the non-productive time and, thus, decrease the total drilling cost. With the recent development of drilling technology and innovation of down-hole signal transmitting method, abundant drilling data are collected and stored in the electronic driller's database. The availability of such data provides new opportunities for rapid and accurate fault detection; however, data-driven fault detection has seen limited practical application in well drilling processes. One particular concern is how to distinguish 'controllable' process changes, e.g., due to set-point changes, from truly abnormal events that should be considered as faults. This is highly relevant for the managed pressure drilling technology, where the operating pressure window is often narrow resulting in necessary set-point changes at different depths. However, the classical data-driven fault detection methods, such as principal component analysis and independent component analysis, are unable to distinguish normal set-point changes from abnormal faults. To address this challenge, a slow feature analysis (SFA)-based fault detection method is applied. The SFA-based method furnishes four monitoring charts containing more information that could be synthetically utilized to correctly differentiate set-point changes from faults. Furthermore, the evaluation about controller performance is provided for drilling operator. Simulation studies with a commercial high-fidelity simulator, Drillbench, demonstrate the effectiveness of the introduced approach.
© 2013 IEEE.

ISSN号：2169-3536

卷、期、页：v 6,p34262-34271

发表日期：2018-06-12

影响因子：3.244000

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）,ESI（基本科学指标数据库）,EI（工程索引）

发表期刊名称：IEEE Access

参与作者：李海寿,王宇红,陈韬,钟磊

第一作者：高小永,左信

论文类型：期刊论文

论文概要：高小永,李海寿,王宇红,陈韬,左信,钟磊，Fault detection in managed pressure drilling using slow feature analysis，IEEE Access，2018，v 6,p34262-34271

论文题目：Fault detection in managed pressure drilling using slow feature analysis