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Density and velocity determination for single-phase flow based on radiotracer technique and neural networks

机译:基于放射体制技术和神经网络的单相流体密度和速度测定

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摘要

Measuring the density and velocity of fluids is one of the important tasks in oil and petroleum industries. The article demonstrates the measurements of these parameters precisely for different fluids and various diameters of pipes by using radiotracer injection and Artificial Neural Network (ANN). The required data for training and testing the ANN model were obtained by the MCNPX code simulations. Before using the simulation results for training the ANN, simulation geometry was validated with an experimental setup. The experimental setup consists of two 2-inch NaI(Tl) detectors that are positioned in distance of 120 mm from each other and one Ba-133 radioactive source as a tracer. It is shown that the estimated Mean Relative Error (MRE) of the density determination in presented system was less than 0.9%. The relative combined standard uncertainty of the fluid velocity measurement did not exceed 0.5%.
机译:测量流体的密度和速度是石油和石油行业的重要任务之一。 本文通过使用放射性机构注射和人工神经网络(ANN)精确地表明这些参数的测量和各种管道的各种管道。 通过MCNPX代码模拟获得了培训和测试ANN模型的所需数据。 在使用训练ANN的仿真结果之前,用实验设置验证了仿真几何。 实验装置由两个2英寸Nai(TL)检测器组成,该探测器位于彼此距离120mm的距离和作为示踪剂的一个BA-133放射源的距离。 结果表明,呈现的系统密度测定的估计平均相对误差(MRE)小于0.9%。 流体速度测量的相对组合的标准不确定性不超过0.5%。

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