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

Abstract

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.

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

“…Persistent lack of physical comprehension continuously stymies preferable prediction performance of the key parameters in multiphase flow and reactor systems, although scientists have made systematic contributions to experimentally formulated correlations throughout the past decades. − The correlations of the key parameters in multiphase units are commonly expressed by gas/liquid/solid phase properties, operating conditions (e.g., phase concentration, velocity, and temperature), devices configurations (e.g., height and diameter), or a combination of them in dimensionless forms such as Archimedes, Froude, Nusselt, Reynolds, Sherwood, and Weber numbers. However, the prediction discrepancies between the existing empirical correlations of key parameters such as the particle entrainment and minimum fluidization velocity in gas-particle riser flows can reach several orders of magnitude. , Fortunately, the advanced research and development of flexible ML tools have the potential to complement the incomplete knowledge to boost the prediction ability of key multiphase field parameters such as mass flow rate/flux, − minimum fluidization velocity, , mixing rate/index, , overall/local hold-up, − pressure/pressure drop, − velocity, ,− temperature, − and other parameters − in multiphase/particulate flows and reactors.…”

From Static to Dynamic: A Review on the Role of Mucus Heterogeneity in Particle and Microbial Transport

“…Machine learning (ML) is adopted to generate robust and advanced prediction models using high-throughput data (from public database or previous studies, etc.) as training sets to mimic the particle transport through the McLs, , which current mathematical tools are not capable of solving. , It is increasingly accepted that ML is a more suitable approach (designed by suitable algorithm and training data sets) than traditional lab studies to observe the large-scale number of particles transported through the heterogeneous McLs and explore the mechanism of free and passive diffusion. Olumegbon et al developed a support vector regression-based model to predict the viscosity of carbon nanoparticles dispersed in the diesel oil.…”

Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines

“…In [ 14 ], an artificial neural network trained with simulated data was used to detect and classify acoustic events, such as footsteps and vehicles. In [ 15 ], different ML techniques (including ANNs and SVMs) were applied to the characterization of a fluid flow in pipes. A comprehensive review on the integration of ML algorithms with distributed optical fiber sensor technologies can be found in [ 16 ].…”