利用数字孪生技术自动实时监测钻井,提高了安全性,降低了成本

R. Rommetveit, M. G. Mayani, J. Nabavi, Stig Helgeland, Raymond Hammer, Jostein Råen
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引用次数: 4

摘要

作为油气行业数字化转型的一部分,油井建设正在朝着使用数字孪生井的高效新方法发展。本文将重点介绍如何监测钻井作业,如何利用井的数字孪生,以及如何将学习到的知识应用于未来的井。数字孪生是资产、系统和流程的数字副本。钻井中的数字孪生是整个钻井生命周期中物理井的精确数字复制品。它的功能是基于先进的液压和动态模型的实时处理。通过利用井中的实时数据,它可以自动分析数据和监测钻井作业,并提供早期诊断信息,以发现问题或事故的早期迹象。在本研究中,将介绍不同井的各种实际操作案例。这包括在具有挑战性的钻井环境中使用数字孪生技术,例如在使用MPD技术时。此外,还将提供各种诊断信息,这些信息可以在井中下入、出井和钻井过程中给出问题的早期迹象。通过实时值和瞬态建模结果的比较,检测到高限制。这些将被讨论。研究了不同的真实案例。将数字RT建模和实时测量数据与预测诊断信息相结合,可以改善决策,减少非生产时间,实现更优的钻井作业。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Realtime Monitoring of Drilling Using Digital Twin Technologies Enhance Safety and Reduce Costs
As part of the digital transformation in oil and gas industry, well construction move toward new efficient methods using digital twins of the wells. This paper will highlight how the drilling operations are monitored, how a digital twin of the well is utilized and how learnings are implemented for future wells. A Digital Twin is a digital copy of assets, systems and processes. A Digital Twin in drilling is an exact digital replica of the physical well during the whole drilling life cycle. Its functionality is based on advanced hydraulic and dynamic models processing in real time. By utilizing real-time data from the well, it enables automatic analysis of data and monitoring of the drilling operation and offer early diagnostic messages to detect early signs of problems or incidents. In the current study various actual operational cases will be presented related to different wells. This includes using digital twin during drilling under challenging circumstances such as conditions when using MPD techniques. Also, various diagnostic messages which gave early signs of problems during running in the hole, pulling out of the hole and drilling will be presented. High restrictions were detected using comparisons of real-time values and transient modelling results. These will be discussed. Different real cases have been studied. Combining digital RT modelled and real-time measured data in combination with predictive diagnostic messages will improve the decision making and result in less non-productive time and more optimal drilling operations.
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