Revolutionizing Oil and Gas Production State Diagnosis with Digital Twin and Deep Learning Fusion Technology

Abstract Digital twins have become an effective tool for monitoring and diagnosing the state of oil and gas production processes. However, due to the complexity of production processes, traditional digital twin models may perform poorly. To address these issues, an improved long and short-term memory (LSTM) neural network is introduced to digital twins to provide real-time diagnosis capabilities for the oil and gas production process. First, a digital twin model is constructed based on the physical model of oil and gas production processes to simulate the behavior of the real system. Surface data is then employed to estimate well data, which is subsequently used to train the LSTM neural network. The trained neural network is used to analyze real-time data collected from sensors installed in the physical system and update the digital twin model accordingly. By comparing the behavior of the real system and the digital twin model, any deviation can be identified, and the state of oil and gas production can be diagnosed. Additionally, the improved neural network optimizes the performance of the digital twin model by reducing the impact of complex production processes and improving the accuracy and efficiency of diagnosis. In the same block or well during different periods, the long-short memory network is used to predict the next state of oil and gas production based on real-time data, enabling the process to realize the deep fusion of physical layer and information layer data, and attain self-perception and self-prediction abilities. The results show that the proposed method effectively monitors and predicts the operational state of oil and gas production, providing critical data for improving production efficiency. Digital twins and deep learning technology can improve the intelligence of the oil and gas production process and provide theoretical support for the development of intelligent oil and gas fields in the future.