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A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase

Hasan Asy'ari Arief1, Tomasz Wiktorski2, Peter James Thomas1

  • 1NORCE Norwegian Research Centre AS, 5008 Bergen, Norway.

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Summary
This summary is machine-generated.

Distributed fibre optic sensing offers real-time multiphase fluid flow monitoring for industrial applications. This review details data analysis techniques, including machine learning, to convert sensor data into actionable insights for optimizing production.

Keywords:
distributed acoustic sensordistributed temperature sensormachine learningmultiphase fluid flowspeed of sound

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Area of Science:

  • Engineering
  • Physics
  • Data Science

Background:

  • Real-time monitoring of multiphase fluid flows is crucial for industrial applications, particularly in optimizing hydrocarbon production and extending reservoir operational lifetime.
  • Distributed fibre optic sensing (DFOS) is a well-developed technology for flow measurement, but robust data analysis tools are needed to interpret the vast amounts of data generated.
  • Converting DFOS data into actionable process indicators remains a key challenge for effective industrial deployment.

Purpose of the Study:

  • To provide a comprehensive technical review of data analysis techniques for distributed fibre optic sensing technologies.
  • To focus on methods for characterizing fluid flow in pipes using DFOS data.
  • To guide end-users in establishing reliable, robust, and accurate solutions for real-time flow monitoring.

Main Methods:

  • Review of classical data analysis methods for DFOS, including speed of sound estimation and Joule-Thomson coefficient.
  • Exploration of data-driven machine learning approaches, such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Ensemble Kalman Filters (EnKF).
  • Analysis of techniques for converting large volumes of sensor data into meaningful process indicators.

Main Results:

  • Classical and machine learning methods offer diverse approaches to analyze DFOS data for fluid flow characterization.
  • Machine learning algorithms like CNN, SVM, and EnKF show promise for advanced data interpretation and real-time analysis.
  • The review highlights the need for robust analytical frameworks to bridge the gap between raw sensor data and practical industrial application.

Conclusions:

  • Effective data analysis is critical for realizing the full potential of distributed fibre optic sensing in industrial flow measurement.
  • A combination of classical and advanced machine learning techniques can provide reliable and accurate solutions for multiphase flow characterization.
  • This review aims to facilitate the timely and effective deployment of DFOS solutions, driving future innovations in the field.