Measurement of Air-Water Counter-Current Flow Rates in Vertical Annulus Using Multiple Differential Pressure Signals and Machine Learning

IF 2.7 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Feng Cao, Ruirong Dang, Bo Dang, Huifeng Zheng, A. Ji, Zhanjun Chen
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引用次数: 0

Abstract

Gas-liquid counter-current flow in vertical annulus is involved in multiple industrial fields such as petroleum engineering. For instance, in coalbed methane wells where liquid pumping is utilized, obtaining real-time gas-liquid flow in the annulus is crucial for the development and management of coalbed methane wells. However, due to complex flow conditions, this requirement is difficult to achieve through traditional flow measurement means. Therefore, this paper proposes a flow prediction method based on multiple sets of differential pressure signals and machine learning techniques. Experiments on air-water two-phase flow were conducted on a vertical annulus pipe with an inner/outer diameter of 75mm/125mm and adjustable eccentricity. The probability density function and power spectral density function of three sets of differential pressure signals collected at different heights in the annulus pipe were used as model inputs, and gas-liquid flow rate as output. A gas-liquid two-phase flow prediction model was constructed based on the artificial neural network model, and the hyper-parameters of the model were optimized using Bayesian optimization. The results show that on a test dataset of 440 combinations of conditions with air superficial velocity of 0.06~5.04m/s, water superficial velocity of 0.03~0.25m/s, and pipe eccentricity of 0, 0.25, 0.5, 0.75, 1, the model can achieve average prediction errors of 9.12% and 29.34% for gas and water flow, respectively. This indicates that the method can be applied to non-throttling, non-intrusive measurement of phase flow under annulus gas-liquid counter-current flow conditions.
利用多重压差信号和机器学习测量垂直环流中的气水逆流流速
垂直环流中的气液逆流涉及石油工程等多个工业领域。例如,在使用液体泵的煤层气井中,实时获取环空中的气液流动对煤层气井的开发和管理至关重要。然而,由于流动条件复杂,传统的流量测量手段难以实现这一要求。因此,本文提出了一种基于多组差压信号和机器学习技术的流量预测方法。在内径/外径分别为 75 毫米/125 毫米、偏心率可调的垂直环形管道上进行了气水两相流实验。在环形管道不同高度采集的三组压差信号的概率密度函数和功率谱密度函数被用作模型输入,气液流量被用作输出。基于人工神经网络模型构建了气液两相流预测模型,并利用贝叶斯优化法对模型的超参数进行了优化。结果表明,在空气表层速度为 0.06~5.04m/s,水表层速度为 0.03~0.25m/s,管道偏心率为 0、0.25、0.5、0.75、1 的 440 种工况组合的测试数据集上,该模型对气体和水流量的平均预测误差分别为 9.12% 和 29.34%。这表明该方法可用于环形气液逆流条件下的非节流、非侵入式相流测量。
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来源期刊
Measurement Science and Technology
Measurement Science and Technology 工程技术-工程:综合
CiteScore
4.30
自引率
16.70%
发文量
656
审稿时长
4.9 months
期刊介绍: Measurement Science and Technology publishes articles on new measurement techniques and associated instrumentation. Papers that describe experiments must represent an advance in measurement science or measurement technique rather than the application of established experimental technique. Bearing in mind the multidisciplinary nature of the journal, authors must provide an introduction to their work that makes clear the novelty, significance, broader relevance of their work in a measurement context and relevance to the readership of Measurement Science and Technology. All submitted articles should contain consideration of the uncertainty, precision and/or accuracy of the measurements presented. Subject coverage includes the theory, practice and application of measurement in physics, chemistry, engineering and the environmental and life sciences from inception to commercial exploitation. Publications in the journal should emphasize the novelty of reported methods, characterize them and demonstrate their performance using examples or applications.
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