In the oil and gas industry, reducing costs and improving data interpretation accuracy pose significant challenges, especially in mature fields like Balsam Field located onshore in the Nile Delta. These challenges are particularly evident during critical decisions on drilling new wells within geological units defined by conventional sedimentological studies. This study focuses on the application of Convolutional Neural Network (CNN) techniques, known for their exceptional performance in pattern recognition and classification, to predict borehole image facies efficiently and accurately within the Qawasim Formation at Balsam Field. The workflow comprises five major steps: data collection, preprocessing, CNN model training, testing, and evaluation. The dataset used includes 1350 images categorized into three labeled facies types (cross-laminated, laminated, and massive). The trained CNN model employs convolutional and max-pooling filters for feature extraction, followed by fully connected neural network layers for classification. The model achieved a significant accuracy of 82%, demonstrating its effectiveness in rapid facies prediction to support real-time decision-making and cost reduction strategies in borehole image analysis. Moreover, this adaptable model can be extended to other clastic reservoirs, offering quick and accurate geological models essential for future field development planning and production optimization. The application of deep learning, as illustrated in this study, enhances both efficiency and accuracy in borehole image interpretation, thereby reducing geological study costs and minimizing risks associated with reservoir modeling.