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在使用放射性粒子跟踪和深度学习的双相系统中确定流速

Roos Sophia de Freitas Dam¹, William Luna Salgado¹, Eddie Jesús Avilán Puertas¹

¹Instituto de Engenharia Nuclear, Divisão de Radiofármacos (IEN / DIRAD), Rua Hélio de Almeida, 75, Cidade Universitária, RJ, 21941-906, Brazil.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

|February 22, 2026

概括

本研究介绍了一种非侵入性的放射性颗粒追踪方法,用于精确测量管道中的石油流量. 该技术准确地预测流体分数和速度,最大限度地降低运营成本和系统停机时间.

科学领域:

核工程核工程是指核工程.
石油工程是石油工程中的一个.
计算流体动力学的流体动力学.

背景情况:

精确的流量测定对于石油行业的运营效率至关重要.
传统的流量计通常需要直接接触流体,导致维护问题和成本增加.
需要使用非侵入性方法来克服传统流量计的局限性.

研究的目的:

建议在双相系统中采用最少干扰的方法来确定流量.
使用放射性粒子跟踪来预测流体体积分和计算表面速度.
为了降低与传统流量计相关的运营成本和系统关闭.

主要方法:

使用密封的-137源来追踪放射性粒子.
在PVC管道中模拟了分层的盐水-油流,使用MCNP6蒙特卡罗代码.
使用深度神经网络进行体积分数预测和速度计算的交叉相关性.

主要成果:

实现了液体体积分的准确预测.
使用油相信号交叉相关的时间延迟计算表面速度.
与理论值相比,油流率的最大平均绝对百分比误差 (MAPE) 为2.24%.

结论:

放射性颗粒追踪技术为石油流速的确定提供了一个可行的,最小的侵入性解决方案.
深度神经网络和交叉相关性有效地处理了放射性信号,以进行准确的流量分析.
拟议的方法显示出高精度和降低石油行业成本的潜力.

关键词:

深度神经网络是一个神经网络.流量流量流量流量流量玛密度测量 (Gamma densitometry) 是一种测量方法,用于测量玛密度.在MCNP6代码中.表面速度是表面的速度.体积分数的体积分数是什么

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