Arief Hasan Asy'ari, Wiktorski Tomasz, Thomas Peter James
NORCE Norwegian Research Centre AS, 5008 Bergen, Norway.
Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
Sensors (Basel). 2021 Apr 15;21(8):2801. doi: 10.3390/s21082801.
Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.
利用分布式光纤传感对多相流体流动进行实时监测，在工业流量测量应用中具有发挥重要作用的潜力。其中一个应用是优化碳氢化合物生产，以实现短期收入最大化，并延长生产井和油藏的运营寿命。虽然测量技术本身已得到充分理解和发展，但一个关键的遗留挑战是建立强大的数据分析工具，这些工具能够将大量数据实时转换为可操作的过程指标。本文对分布式光纤技术的数据分析技术进行了全面的技术综述，特别关注管道内流体流动的表征。综述涵盖了经典方法，如声速估计和焦耳-汤姆逊系数，以及它们的数据驱动机器学习对应方法，如卷积神经网络（CNN）、支持向量机（SVM）和集合卡尔曼滤波器（EnKF）算法。该研究旨在帮助终端用户建立可靠、稳健且准确的解决方案，这些方案能够及时有效地部署，并为该领域的未来发展铺平道路。
