Harnessing Multiple Time-Series Sensor Data: Evaluating the Efficacy of Various Machine Learning Models in Predicting Gas-Water Two-Phase Flow

M Bao, R Wu, M Wang, K Li - ASTFE Digital Library, 2024 - dl.astfe.org
Multiphase flow Measurement is pivotal in myriad industrial applications, including but not
limited to oil and gas production, chemical engineering, and environmental monitoring …

Enhancing Accuracy in Gas-Water Two-Phase Flow Sensor Systems Through Deep-Learning-based Computational Framework

M Bao, R Wu, M Wang, K Li, X Jia - Authorea Preprints, 2024 - techrxiv.org
Multiphase flow is a critical component in contemporary industrial operations, yet the
accurate quantification of multiphase parameters presents a substantial obstacle. This study …

[HTML][HTML] Machine learning for multiphase flowrate estimation with time series sensing data

H Wang, M Zhang, Y Yang - Measurement: Sensors, 2020 - Elsevier
In this paper, we investigate the prediction of multiphase flowrate based on multi-modal time
series sensing data by using machine learning. The time series differential pressure data …

Multiphase flow measurement and data analytic based on multi-modal sensors

H Wang - 2023 - era.ed.ac.uk
Accurate multiphase flow measurement is crucial in the energy industry. Over the past
decades, separation of the multiphase flow into single-phase flows has been a standard …

Multiphase flowrate measurement with multimodal sensors and temporal convolutional network

H Wang, D Hu, M Zhang, N Li, Y Yang - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Accurate multiphase flow measurement is vital in monitoring and optimizing various
production processes. Deep learning has as of late arose as a promising approach for …

[HTML][HTML] A Pressure-Based Multiphase Flowmeter: Proof of Concept

V Ramakrishnan, M Arsalan - Sensors, 2023 - mdpi.com
Multiphase flowmeters (MPFMs) measure the flow rates of oil, gas, and brine in a pipeline.
MPFMs provide remote access to real-time well production data that are essential for …

[HTML][HTML] Deep learning models for improved accuracy of a multiphase flowmeter

M Manami, S Seddighi, R Örlü - Measurement, 2023 - Elsevier
Measurement of oil and gas two-phase flow with variable flow regimes relies to a large
extent on flow patterns and their transitions. Using multiphase flowmeters in flows with high …

Comparison of machine learning methods for multiphase flowrate prediction

Z Jiang, H Wang, Y Yang, Y Li - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
In this paper, three prevailing machine learning methods, ie Deep Neural Network (DNN),
Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) models were …

Ensemble learning in the estimation of flow types and velocities of individual phases in multiphase flow using non-intrusive accelerometers' and process pressure data

R Yan, H Viumdal, K Fjalestad… - 2022 IEEE Sensors …, 2022 - ieeexplore.ieee.org
in oil and gas industries. Accurately identifying flow types and estimating flow velocities of
the individual phases are crucial for different purposes, such as observing the process status …

Multiphase flow rate prediction using chained multi-output regression models

MF Wahid, R Tafreshi, Z Khan, A Retnanto - Geoenergy Science and …, 2023 - Elsevier
Virtual flow meters (VFM) are emerging as an attractive and cost-effective alternative to
traditional multiphase flow meters to meet monitoring demands, reduce operational costs …