Study on High-Performance Prediction of Wellbore Multiphase Flow Pressure Drop Distribution Based on Artificial Neural Networks
Xi Ouyang1,2, Xiang Rao1,2,3*
1 School of Petroleum Engineering, Yangtze University, Wuhan 430100, China. 2 State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization (Yangtze University), Wuhan 430100, China. 3 Western Research Institute, Yangtze University, Karamay 834000, China.
This paper constructs a proxy model to predict the pressure drop distribution in wellbore multiphase flow based on artificial neural networks (ANNs). 10,000 sets of high-quality samples involving 15 parameters involving wellbore multiphase flow and covering diverse working conditions were generated based on the Beggs-Brill algorithm, which ensures the physical consistency of data and the comprehensiveness of working conditions; two ANN models were constructed to realize the bidirectional nonlinear mapping from wellhead parameters to bottom-hole pressure and from bottom-hole parameters to wellhead pressure, respectively. Case verification results show that the mean relative error of the forward ANN model is only 0.10%, that of the reverse ANN model is 0.15%, and the prediction time for a single sample is less than 0.002 seconds for both models. The research results provide an efficient and reliable technical tool for the dynamic production regulation of oil and gas fields and the rapid evaluation of multi-well development schemes, helping to improve the recovery efficiency and economic benefits of oil and gas exploitation.
Keywords: Deep Learning; Wellbore multiphase flow; Pressure drop prediction; Neural network; Beggs-Brill algorithm; Coupled simulation; Machine learning
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Funding
The General Program of the National Natural Science Foundation of China (Grant No. 52574028)
The Youth Science Fund Program (Category A) of the National Natural Science Foundation of China (Grant No. 52525403)
The “Science and Technology Innovation Team” Program of the Xinjiang Uygur Autonomous Region (Grant No. 2024TSYCTD0018).
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