Real-Time Well Monitoring and Engineering Analysis of Drilling Activities: Intelligent Rig State Detection and Prediction With Uncertainty

Kemajou Vanessa Ndonhong, Robello Samuel
{"title":"Real-Time Well Monitoring and Engineering Analysis of Drilling Activities: Intelligent Rig State Detection and Prediction With Uncertainty","authors":"Kemajou Vanessa Ndonhong, Robello Samuel","doi":"10.1115/omae2020-18060","DOIUrl":null,"url":null,"abstract":"\n Drilling activities are risky and costly, especially when performed offshore. Careful monitoring and real time data analysis are required for safe and efficient operations with minimized down-time. Drilling operations, being fast-paced and not visible, often lead to transient and unforeseen issues. The synchronous assessment and prediction of drilling quality has historically been a challenge. It relies on a prompt collection, analysis and prediction of the multiple sensors data, as well as an immediate comparison to the original drilling plan. Another challenge is achieving real-time well engineering, and automatically and instantaneously providing valuable insights to the engineering and operations teams. A system was successfully developed to tackle these challenges. It is a cloud-based application, made with an event-driven streaming architecture to automatically retrieve real-time drilling data and compare it with planned data. The real-time data is automatically made available to determine the current well operation or rig state, and trigger the subsequent engineering analysis. Next, a forecast model is trained with the engineering calculation outputs and it returns predictions on these outputs while considering their inherent uncertainty. As a result, these predictions enable alerts to be sent when the system detects approaching anomalous conditions. The proposed system is a DecisionSpace® 365 cloud-native application on an open architecture. It is flexible, accessible from anywhere, can be automatically updated for continuous improvement, and can be deployed easily and quickly. It can also be extended to further applications.","PeriodicalId":403225,"journal":{"name":"Volume 11: Petroleum Technology","volume":"32 1","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/omae2020-18060","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

Drilling activities are risky and costly, especially when performed offshore. Careful monitoring and real time data analysis are required for safe and efficient operations with minimized down-time. Drilling operations, being fast-paced and not visible, often lead to transient and unforeseen issues. The synchronous assessment and prediction of drilling quality has historically been a challenge. It relies on a prompt collection, analysis and prediction of the multiple sensors data, as well as an immediate comparison to the original drilling plan. Another challenge is achieving real-time well engineering, and automatically and instantaneously providing valuable insights to the engineering and operations teams. A system was successfully developed to tackle these challenges. It is a cloud-based application, made with an event-driven streaming architecture to automatically retrieve real-time drilling data and compare it with planned data. The real-time data is automatically made available to determine the current well operation or rig state, and trigger the subsequent engineering analysis. Next, a forecast model is trained with the engineering calculation outputs and it returns predictions on these outputs while considering their inherent uncertainty. As a result, these predictions enable alerts to be sent when the system detects approaching anomalous conditions. The proposed system is a DecisionSpace® 365 cloud-native application on an open architecture. It is flexible, accessible from anywhere, can be automatically updated for continuous improvement, and can be deployed easily and quickly. It can also be extended to further applications.
钻井活动的实时井监测和工程分析:不确定性的智能钻机状态检测和预测
钻井作业风险大、成本高,尤其是在海上作业时。需要仔细监控和实时数据分析,以实现安全高效的操作,最大限度地减少停机时间。钻井作业节奏快、不可见,往往会导致短暂的、不可预见的问题。钻井质量的同步评估和预测一直是一个挑战。它依赖于对多个传感器数据的快速收集、分析和预测,以及与原始钻井计划的即时比较。另一个挑战是实现实时井工程,并自动、即时地为工程和运营团队提供有价值的见解。我们成功开发了一个系统来应对这些挑战。这是一款基于云的应用程序,采用事件驱动的流架构,可以自动检索实时钻井数据,并将其与计划数据进行比较。实时数据可自动用于确定当前的井况或钻机状态,并触发后续的工程分析。接下来,使用工程计算输出训练预测模型,并在考虑其固有不确定性的情况下返回这些输出的预测。因此,当系统检测到接近异常情况时,这些预测可以发送警报。提议的系统是一个开放架构上的DecisionSpace®365云原生应用程序。它非常灵活,可以从任何地方访问,可以自动更新以进行持续改进,并且可以轻松快速地部署。它还可以扩展到其他应用。
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