Application of Convolutional Neural Network in SCADA System Assisted Diagnosis of Oil Well Indicator Diagram

With the progress of oilfield development work, mechanical oil extraction has become the main oil extraction method in oilfields. Due to its unique working environment, oil well failures often occur, resulting in a decrease in production and efficiency. In response to the problems of multiple daily testing wells, one-sided manual and single diagnosis, and the inability to timely and effectively detect whether the oil well is normal, this article uses the classic model InceptionV3 of convolutional neural networks to train the convolutional neural network model by establishing indicator diagram training data for each working condition. During the model establishment process, the training parameters and sample set data are continuously modified, Finally, convolutional neural networks were implemented for auxiliary diagnosis in SCADA systems. Not only does it reduce labor intensity, but it also improves the efficiency and accuracy of indicator diagram diagnosis, achieving accurate analysis of oil well production dynamics and timely making correct adjustments.