Research on the Application of Artificial Intelligence in Geological Logging Data Analysis

Geological well logging data serves as an essential basis for oil and gas exploration and development. However, traditional interpretation methods face challenges such as low manual efficiency, strong subjectivity, and data loss. This research aims to explore the application of deep learning techniques in intelligent recognition and prediction of geological well logging information to improve the automation level and accuracy of logging data interpretation. The study designs a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). It extracts local morphological features of logging curves through multi-scale parallel CNN, introduces an attention mechanism for dynamic weighting of different feature channels, and captures the depth dependence of logging data using LSTM/GRU units. Experiments were conducted using actual logging data from 10 wells in an oil field. Results show that in logging curve prediction tasks, the proposed model reduces mean square error by more than 20% compared to single network structures, with a trend consistency of 91.2%. In reservoir fluid identification tasks, the hybrid model achieves an accuracy of 89.1%, improving the identification accuracy of difficult-to-identify oil-water layers by approximately 3%. Comparative experiments prove that multi-scale feature extraction and attention mechanisms significantly enhance model performance, while weighted loss functions effectively solve class imbalance problems. This research innovatively combines spatial feature extraction with temporal dependency learning, proposing a deep learning architecture suitable for geological logging data, providing efficient and accurate intelligent interpretation methods for petroleum exploration and development, which is significant for improving oil and gas reservoir evaluation precision and exploration success rates.