This work explores the capability of both Type-1 and Type-2 Fuzzy Inference Systems as novel approaches for predicting carbonate lithofacies and permeability from Well Logs. As expected, these new computational intelligence approaches overcome the weaknesses of the standard neural networks limitations. In addition, we carried out a comparative study to compare their performances with those of the conventional artificial neural network models and other popular techniques. Empirical results show that the performance of T1&T2 FIS novel approaches outperform most of the common existing approaches, particularly in the area of ability to handle data in uncertain situations, which are the common characteristics of well logs data. As an additional advantage of this work, a Type-2 fuzzy logic based model has been developed that will generate, not only the target forecast, but also prediction intervals without extra compuational cost. The methodology involved were described and We have developed smart simulation structure to attack such problem with real-industry data to prove our contribution.