Deep Learning Approaches for Facies Prediction in Subsurface Seismic Data

Abstract Hydrocarbon exploration and production depend on the classification of seismic facies, a basic component of subsurface reservoir characterization. Conventional interpretation techniques mostly rely on subjective, time-consuming, and inconsistent manual examination of seismic data. Automated methods have become effective instruments for increasing seismic interpretation efficiency and accuracy as deep learning has advanced. In this work, a deep learning framework for classifying seismic facies using a Convolutional Neural Network (CNN) based on U-Net is presented. To assess the model's prediction power, it was tested on an unlabeled dataset after being trained on labeled seismic data. To improve model performance, data preprocessing methods such as one-hot encoding, normalization, and image scaling were used. Because of the magnitude of the dataset and the computational complexity, training was done on Google Collab. Techniques for data augmentation were also used to enhance generalization. Numerous performance measures were used to assess the algorithm, proving its efficacy in classifying facies. The findings show that by automating classification, decreasing manual labor, and enhancing the dependability of subsurface analysis, deep learning considerably improves seismic interpretation. The trained model's potential for scalable deployment in oil and gas exploration was demonstrated by the fact that it was preserved for later use.