Research on Efficiency Driven Classification in Petroleum Engineering Based on Big Data Algorithm

Existing petroleum engineering big data algorithms have issues like poor efficiency, insufficient classification accuracy, and poor algorithm adaptability in classification tasks by application field. In order to resolve these issues, this paper proposes an RNN (Recurrent Neural Network) algorithm, which improves the performance of the model in multi-category classification by introducing a combination of ReLU activation function and Softmax output layer. By extracting features and optimizing data from different application scenarios in the field of petroleum engineering, the algorithm effectively improves the classification accuracy and application efficiency of the model. Specifically, this paper uses different application fields in petroleum engineering big data as classification labels, uses the architecture of a neural network with multiple layers, and combines it with the Adam optimizer to improve the training speed and stability of the model by adjusting and fine-tuning the model parameters layer by layer. In the training process at each stage, special emphasis is placed on the adjustment of hyperparameters and the alleviation of the gradient vanishing problem, guaranteeing the effectiveness and precision of the classification results in multi-domain data. The findings from the experiments demonstrate that the enhanced algorithm has strong future potential and practical value, and that it can effectively boost the computation efficiency of huge amounts of data in oil and gas engineering as well as the accuracy of classification assignments in real-world applications. In the comparison of different activation functions, the ReLU activation function (improved model) performed best, with a classification accuracy of 0.852, a training time of 125 seconds, an F1-Score of 0.81, and an AUC-ROC of 0.9.