Exploring hydrocarbon in structural-stratigraphical traps is challenging due to the high lateral variation of lithofluid facies. In addition, reservoir characterization is getting more obscure if the reservoir layers are thin and below the seismic vertical resolution. Our objectives are to reduce the uncertainty of reserve estimation and to predict hydrocarbon distribution more accurately in such reservoir layers by proposing a new workflow that works better than the conventional one. The approach was performed by integrating petroelastic modeling, stochastic elastic seismic inversion, and Bayesian probability classification in the upper reservoir layer of Group E in the Northern Malay Basin. A robust petroelastic model was initially built to obtain more obvious separation of different lithofluid classes in elastic properties crossplot, that is acoustic impedance versus ratio. To achieve reliable distribution of elastic properties per identified lithofluid class, a Monte Carlo simulation was then run and the posterior probability of all classes was computed using Bayesian classification, followed by confusion matrix assessment. Stochastic elastic seismic inversion was carried out on conditioned seismic data to predict elastic properties away from the wells. Using all elastic properties realizations, ranking was calculated and uncertainty was quantified at the blind well location. The most probable scenario is the realization that has a much closer probability to the measured criterion value at the blind well. The computed posterior probability of hydrocarbon-bearing sand was applied on the selected stochastic realization (acoustic impedance and volumes) according to the ranking result. Finally, the hydrocarbon distribution probability map was generated and validated with lithofluid facies information of four distributed wells. Such a comparison authenticated the hydrocarbon prediction particularly at the blind well location.