基于GWO-ANN的气固两相流出砂监测方法研究

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中图分类号：TB9;TP183 文献标志码：A文章编号：1674-5124（2026）02-0034-06

Abstract：With the rapid development and increasing demand in the global oil and gas industry，sand production issues have a significant impact on equipment and pipelines， leading to reduced production efficiency and increased safety risks.To accurately predict sand production volume and reduce the risks and costs associated with oil and gas extraction， a sand production volume prediction model combining the grey wolf optimizer （GWO） and artificial neural network （ANN） is proposed. Addressing the problem of large errors in traditional models， the proposed GWO-ANN sand production volume prediction model optimizes the weights and biases of the neural network using the grey wolf optimizer， which can improve the prediction accuracy and robustness of the model. In the experimental design section，sand production signals from gassand two-phase flow were colected using vibration sensors， and the Hilbert-Huang transform （HHT） was used to analyze the frequency band characteristics of the sand production signals.A finite impulse response （FIR）

filter was employed to remove noise. Principal component analysis （PCA） was used to reduce the complexity of signal features，and the main features were input into the GWO-ANN model for training and prediction. Experimental results show that the GWO-ANN model has a small maximum relative error on the test set， indicating that the GWO-ANN model can eectively monitor sand production volume with high accuracy and reliability.

Keywords： prediction of sand production； gas-solid two-phase flow; artificial neural network; grey wolf optimizer

0引言

多年来，随着天然气和石化产品需求的增长，全球石油和天然气行业快速发展[1]。（剩余8486字）