Abstract: This study aims to improve the intelligence and accuracy of fault diagnosis in oilfield wastewater systems. A composite neural network is constructed using convolutional neural networks and long short-term memory networks, and the structure is optimized using Adam and random gradient descent method to improve the convergence speed and fault diagnosis accuracy of the model. The study is validated through relevant experiments, and the experimental results show that the optimization algorithm used in the study improves the accuracy of the model to around 0. 87 and reduces the diagnostic loss rate of the model to around 0. 032. The average detection accuracy of the composite neural network structure reaches 0. 888, with an accuracy value of 0. 883 and a recall rate of 0. 789. The composite neural networks is applied to fault diagnosis of oilfield wastewater systems, can achieve intelligent fault detection, reduce economic costs, and build smart oilfield.

Key words: convolutional neural networks-long short term memory(CNN-LSTM), composite neural network, sewage system, fault detection, random gradient descent method, smart oilfield