Attempt of a Real-Time Drilling State Identification With Machine Learning

Tomoya Inoue, J. Ishiwata, R. Wada, J. Tahara
{"title":"Attempt of a Real-Time Drilling State Identification With Machine Learning","authors":"Tomoya Inoue, J. Ishiwata, R. Wada, J. Tahara","doi":"10.1115/omae2020-19169","DOIUrl":null,"url":null,"abstract":"\n Japan Agency for Marine-Earth Science and Technology (JAMSTEC) operates the scientific drillship Chikyu for scientific research. However, Chikyu has experienced problems with respect to drill pipe failure. This may be due to the limitation of indication of anomalies such as drill pipe failure in conventional drilling data monitoring.\n As one of primary aims of scientific drilling is to recover core samples from sediment layers under the seabed, improving core recovery rate is very important because it can enhance the operation efficiency. Obtaining the information of lithology of drilling layer is also helpful for both scientific and operational aspects. However, there is no direct information regarding the core recovery rate and lithology. The recovery rate and lithology can be determined after retrieving a coring tool. Therefore, this study applies a machine learning technique to identify the drilling states, which includes anomaly detection of the drilling torque assuming the drill pipe failure, the prediction of core recovery rate as well as lithology.\n This study aims to achieve real-time drilling state identification. Accordingly, a drilling data acquisition and distribution system was developed. The drilling data distributed from the system is read by data analysis systems in several languages for real-time analysis.\n The drilling state identification models created in Python include the anomaly detection model and the prediction models of the core recovery and lithology. The models were installed in the real-time drilling data analyzing system, and real-time drilling state identification was attempted during the operation to confirm the health of the real-time drilling data analyzing system and to demonstrate identification with machine learning.","PeriodicalId":403225,"journal":{"name":"Volume 11: Petroleum Technology","volume":"104 12","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 11: Petroleum Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/omae2020-19169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Japan Agency for Marine-Earth Science and Technology (JAMSTEC) operates the scientific drillship Chikyu for scientific research. However, Chikyu has experienced problems with respect to drill pipe failure. This may be due to the limitation of indication of anomalies such as drill pipe failure in conventional drilling data monitoring. As one of primary aims of scientific drilling is to recover core samples from sediment layers under the seabed, improving core recovery rate is very important because it can enhance the operation efficiency. Obtaining the information of lithology of drilling layer is also helpful for both scientific and operational aspects. However, there is no direct information regarding the core recovery rate and lithology. The recovery rate and lithology can be determined after retrieving a coring tool. Therefore, this study applies a machine learning technique to identify the drilling states, which includes anomaly detection of the drilling torque assuming the drill pipe failure, the prediction of core recovery rate as well as lithology. This study aims to achieve real-time drilling state identification. Accordingly, a drilling data acquisition and distribution system was developed. The drilling data distributed from the system is read by data analysis systems in several languages for real-time analysis. The drilling state identification models created in Python include the anomaly detection model and the prediction models of the core recovery and lithology. The models were installed in the real-time drilling data analyzing system, and real-time drilling state identification was attempted during the operation to confirm the health of the real-time drilling data analyzing system and to demonstrate identification with machine learning.
基于机器学习的钻孔状态实时识别方法的尝试
日本海洋地球科学技术机构(JAMSTEC)为科学研究运营科学钻探船“地球号”。然而,Chikyu在钻杆故障方面遇到了问题。这可能是由于常规钻井数据监测中钻杆失效等异常指示的局限性。科学钻探的主要目的之一是从海底沉积物层中回收岩心样品,提高岩心回收率对提高作业效率具有重要意义。获得钻井层的岩性信息对科学研究和作业都有帮助。然而,没有关于岩心采收率和岩性的直接信息。取心工具后,可以确定采收率和岩性。因此,本研究采用机器学习技术来识别钻井状态,包括假设钻杆失效的钻井扭矩异常检测、岩心采收率预测以及岩性预测。本研究旨在实现实时钻井状态识别。据此,开发了钻井数据采集与分配系统。从系统中分发的钻井数据由多种语言的数据分析系统读取,进行实时分析。在Python中创建的钻井状态识别模型包括异常检测模型、岩心采收率和岩性预测模型。将模型安装在实时钻井数据分析系统中,并在操作过程中尝试进行实时钻井状态识别,以确认实时钻井数据分析系统的健康状况,并演示机器学习识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信