Revolutionizing Oil Production State Diagnosis With Digital Twin and Deep Learning Fusion Technology
As global energy demand continues to grow, the efficiency and safety of oil and gas production systems have become increasingly important. However, the current-state diagnosis technologies in the oil and gas production sector still face multiple challenges. Traditional monitoring methods often rely...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/ijap/6480113 |
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| Summary: | As global energy demand continues to grow, the efficiency and safety of oil and gas production systems have become increasingly important. However, the current-state diagnosis technologies in the oil and gas production sector still face multiple challenges. Traditional monitoring methods often rely on experience and rule-based approaches, making it difficult to reflect the actual operating status of the system in real time. These methods show insufficient accuracy and response speed when dealing with complex operating conditions and sudden events, leading to potential economic losses and safety hazards. Aiming to enhance the real-time diagnostic capabilities of oil and gas production processes, this study introduces an improved long short-term memory (LSTM) neural network into digital twin models. Digital twins have emerged as a potent tool for monitoring and diagnosing the state of oil and gas production processes. However, due to the inherent complexity of these processes, traditional digital twin models often underperform. To address this issue, we propose integrating an advanced LSTM neural network to improve the diagnostic accuracy and efficiency of these models in real-time applications. Initially, a digital twin model is constructed based on the physical model of oil and gas production processes, simulating the behavior of the real system. Surface data are employed to estimate well data, which is subsequently used to train the LSTM neural network. This trained neural network analyzes real-time data collected from sensors installed in the physical system and updates the digital twin model accordingly. By comparing the behavior of the real system with that of the digital twin model, deviations can be identified, allowing for accurate diagnosis of the production state. Furthermore, the improved neural network optimizes the performance of the digital twin model by mitigating the impact of complex production processes, enhancing diagnostic accuracy and efficiency. The LSTM network is utilized to predict the future state of oil and gas production based on real-time data from the same block or well during different periods, enabling deep integration of physical and information layer data, as well as self-perception and self-prediction capabilities. Results demonstrate that the proposed method effectively monitors and predicts the operational state of oil and gas production, providing critical data to improve production efficiency. The integration of digital twins and deep learning technology can enhance the intelligence of oil and gas production processes and offer theoretical support for the development of intelligent oil and gas fields in the future. |
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| ISSN: | 1687-5877 |