Logging procedure and interpretation are complicated mainly because of the large amount of different data inherited. One way to facilitate the problem is to use fewer tools but keep the same amount of information which can be feasible with the assistance of machine learning models. In this paper, Variable Density Logging data was used to evaluate the eccentricity and the acoustic impedance average of the medium behind the casing of oil wells. Different shallow and deep learning architectures were trained, and a Principal Components Analysis feature extraction approach was investigated. Actual data from acoustic logging tools acquired in different wells were adopted. A supervised learning approach was explored, and the K-Nearest Neighbors model obtained the best results when accounting for the cost-benefit and complexity of the models. State-of-the-art hyperparameter optimization techniques were used to tune the non-trainable parameters of the shallow learning algorithms evaluated. The proposed procedure was able to achieve mean balanced adjacency accuracy results greater than 80% for the free pipe pass and repeated pass of a similar well and results greater than 70% for the main pass of a distinguished oil well; which is a more challenging scenario.