Permeability is an essential parameter for the reservoir characteristics, which controls the flowing fluids in the reservoir hence the sweep efficiency and the ultimate recovery. The common practice in the petroleum industry is coring a limited number of wells, due to the expensive core recovery process, and measuring the permeability in the recovered cores then extend the concluded correlation to the un-cored wells. However, establishing a reliable permeability predictor is not an easy task in many heterogeneous formations due to the spatial variability of the permeability even at very close distances. Therefore, the conventional linear regression has often failed to address the formation�s heterogeneity, and an unsatisfied correlation coefficient has frequently obtained. Lower Qamchuqa formation, which is highly prolific producing formation in the Middle East, has been used as an example of highly heterogeneous carbonate systems. Well log measurements, which are available for most of the wells, in addition to the core data, were used to capture the high heterogeneity of the depositional environment. Core-log depth calibration was first performed to extract the accurate log measurements that correspond to the actual core data depth. Then, both core and log data were listed in a table for the statistical analysis using the neural network (NN) and multivariate regression approaches. A remarkable improvement in the correlation coefficient was obtained using the NN approach. The utilised training data and further verified by the validation data set have obtained a favourable accuracy compared with conventional linear regression or multivariate regression. The NN permeability predictor has proven its ability to overcome the complexity of the carbonate rock textures and the variety of the diagenesis alteration processes, which make the NN approach a superior method in obtaining an improved permeability predictor. Nevertheless, a regular update to estimate the permeability predictor would be necessary when new data acquired. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.