Exploring hydrocarbon in structural-stratigraphical traps is challenging due to the high lateral variation of litho-fluid facies. In addition, reservoir characterization is getting more obscure if the reservoir layers are thin and below seismic vertical resolution. Our objectives are to reduce 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 Petro-Elastic modeling, stochastic elastic seismic inversion and Bayesian probability classification in the upper reservoir layer of Group E in the Northern Malay Basin. A robust Petro-Elastic model was initially built to obtain more obvious separation of different litho-fluid classes in elastic properties cross plot i.e. Acoustic Impedance versus VP/VS ratio. To achieve reliable distribution of elastic properties per identified litho-fluid class, 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 blind well. The computed posterior probability of hydrocarbon-bearing sand was applied on the selected stochastic realization (Acoustic Impedance and VP/VS volumes) according to the ranking result. Finally, the hydrocarbon distribution probability map was generated and validated with litho-fluid facies information of four distributed wells. Such comparison authenticated the hydrocarbon prediction particularly at the blind well location. © 2020 Society of Exploration Geophysicists and American Association of Petroleum Geologists.