Mapping facies variations is a fundamental element in the study of reservoir characteristics. From identifying a pay zone to estimating the reservoir capacity, a hydrocarbon field’s development plan depends to a great extent on a reliable model of lithofacies and fluid content variations throughout the reservoir. The starting point usually is creating 1D facies models based on core samples and drilling reports at each well location. Sparse well locations and the inherent heterogeneity in the reservoir properties make it essential to incorporate the resultant 1D models into a 3D model of facies distribution that includes information about the probability of their occurrence. New techniques using machine learning (ML) to build 3D lithofluid facies (LFF) models can incorporate the prediction of different lithofacies regarding their potential hydrocarbon content, along with the uncertainties of the prediction. We have applied a fuzzy inference system, as an expert-oriented approach, and two separate ML algorithms, to different seismic and elastic attributes to model the LFF classes within the Heidrun oil and gas field. The results, compared with the test wells, show that ML methods could successfully predict the distribution of gas and oil sands within the field, in very good agreement with the known fluid contact intervals. Moreover, the predictions of shale and brine sands vary depending on the method but also are consistent with our knowledge of this field. Comparison between the results confirms the higher reliability of ML methods. Importantly, ML methods provide a better way of investigating and quantifying the uncertainty of the predictions. Implementing ML algorithms in reservoir characterization reduces the risk of drilling unnecessary wells due to false discoveries and can lead to more economical development of hydrocarbon resources.