Research on Downhole Two-Phase Flow Parameter Measurement Method Based on Deep Neural Networks
DOI:
https://doi.org/10.54097/0r80e306Keywords:
Deep Neural Networks, Two-Phase Flow, Parameter Measurement, Downhole, Oil and GasAbstract
With the rapid development of the oil and gas industry, accurate measurement of downhole two-phase flow parameters has become particularly critical. However, traditional measurement methods face numerous challenges in complex downhole environments and cannot meet the modern industry's demands for high precision and real-time requirements. The flow behavior of downhole two-phase flows (such as oil-gas, water-gas, etc.) directly affects the production efficiency and safety of oil and gas wells. Measuring key parameters (such as flow rate, gas content, and flow pattern, etc.) is crucial for optimizing production processes, improving recovery rates, and ensuring wellbore safety. Traditional measurement methods, such as mechanical flowmeters and capacitance sensors, are easily affected by various factors such as temperature, pressure, and composition changes in complex downhole environments, leading to inaccurate measurement results. In addition, traditional methods usually require a long response time and cannot meet the modern industry's dual demands for real-time and high precision. Under these circumstances, researching a new, high-precision downhole two-phase flow parameter measurement method has become urgent. With the rapid development of artificial intelligence and machine learning technologies, deep neural networks (DNN) offer a potential solution due to their powerful data processing and non-linear modeling capabilities. Applying deep neural networks to the measurement of downhole two-phase flow parameters can not only overcome the limitations of traditional methods but also significantly improve the accuracy and real-time performance of measurements, providing strong technical support for the oil and gas industry. Therefore, this paper proposes a downhole two-phase flow parameter measurement method that integrates deep neural networks, focusing on the research of multi-phase flow parameter measurement technology based on deep learning and its application in the production process of oil and gas wells. By constructing and optimizing models such as Generative Adversarial Networks (GAN), Bidirectional Long Short-Term Memory Networks (BI-LSTM), and Graph Convolutional Networks (GCN), this paper achieves efficient identification and parameter measurement of downhole two-phase flow pattern characteristics. Experimental results show that the integrated model can effectively predict key parameters of downhole two-phase flow under different working conditions, significantly improving measurement accuracy and robustness. This study not only provides a new solution for the measurement of downhole two-phase flow parameters but also has important significance for improving the automation level of measurement technology, reducing manual intervention, and reducing production costs. It further provides new ideas and methods for solving complex fluid flow problems in the energy field, with broad application prospects and profound practical value.
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