A Novel Multiphase Flow Water Cut Modeling Framework Based on Multilevel Multiscale Convolutional Neural Network

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-08-14 DOI:10.1109/JSEN.2025.3597099

Weidong Dang;Xiaoyang Li;Ruiqi Wang;Haoyu Li;Zhongke Gao

引用次数: 0

Abstract

Water cut measurement is crucial in oil–water multiphase flows, particularly in late-stage oilfield extraction, where high water production presents significant operational challenges. This article proposes a novel multilevel multiscale convolutional neural network (MLMS-CNN) to achieve water cut estimation. The model is designed to extract and analyze complex flow characteristics through three key modules. The multilevel feature learning (MLFL) module fuses spatial and amplitude–phase features from sensor data, while the multiscale feature fusion module captures flow structures across multiple scales. Additionally, the fully convolutional measurement (FCM) module ensures precise water cut prediction. Experimental results demonstrate that the model achieves a mean square error of 0.013%, highlighting its potential for enhancing real-time industrial multiphase flow monitoring and optimization.

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基于多层次多尺度卷积神经网络的多相流含水率建模框架

含水率测量在油水多相流中是至关重要的，特别是在油田开采后期，高含水量给作业带来了重大挑战。本文提出了一种新的多层多尺度卷积神经网络（MLMS-CNN）来实现含水率估计。该模型通过三个关键模块对复杂的流动特性进行提取和分析。多层特征学习（MLFL）模块融合来自传感器数据的空间和幅相特征，而多尺度特征融合模块捕获跨多个尺度的流结构。此外，全卷积测量（FCM）模块可确保精确的含水率预测。实验结果表明，该模型的均方误差为0.013%，显示了其在工业多相流实时监测和优化方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice