Lithofacies Prediction Based on Wavelet Transform and Convolutional Neural Networks

Lithofacies analysis serves as the foundation for identifying high-quality reservoirs. However, in areas devoid of well data or constrained by complex inter-well geological conditions, traditional techniques struggle to rapidly and accurately recognize lithofacies types and their spatial distribution. This paper proposes a convolutional neural network (CNN)-based lithofacies identification method integrated with continuous wavelet transform (CWT), achieving efficient lithofacies recognition through deep learning. Applied to the Karamay formation in the Zhengshacun area of the Junggar Basin, the methodology involves: classifying typical lithofacies based on core and logging characteristics, performing synthetic record-based well-to-seismic matching to align logging lithofacies with post-stack seismic data, converting the matched seismic waveforms into time-frequency spectrum maps using Morlet wavelet transform, generating a time-frequency spectrum dataset for different lithofacies, and constructing and training a CNN model for validation. Under horizon constraints, the planar distribution of various lithofacies is investigated. Results demonstrate that the Morlet-CNN model achieves high identification accuracy, with recognition rates exceeding 85% for 4 lithofacies types in blind well X2, significantly enhancing both the efficiency and accuracy of lithofacies identification.