Well log datasets are often scarce, which hinders the development of machine learning models for reservoir analysis, a common challenge in the oil and gas industry. We present VAEc-tMC, a Conditional Variational Autoencoder designed to generate synthetic well log data conditioned on rock type. By embedding geological context into the generative process, our model addresses a critical gap overlooked by existing methods. Our approach integrates a Student’s t-distribution loss, a smoothed Kullback–Leibler divergence, and low-variance Monte Carlo method sampling to improve robustness and fidelity. When used for data augmentation, the synthetic data preserve key statistical properties of real logs and improve downstream lithology classification by about 80% in AUC, 62% in accuracy, and 71% in F1. These findings validate the model’s ability to generate geologically consistent synthetic data, extending its applicability to reservoir modeling and downstream ML workflows in data scarce environments.