<![CDATA[Development stage of a hydrocarbon field usually aims to discover additional reserves within the working area. In this stage, more data, such as well log and core sample, are available to be included in the development plan compared to early exploration stage. Incorporating the information from well to know the distribution of the prospective zone could be done in many ways. In this paper, the workflow of how information in producing well is utilized to predict the distribution of gas-filled sand by using Bayesian framework is presented. Bayesian frameworks use prior statistical information of the gas sand itself, such as prior probability and likelihood function, in calculating the posterior probability. From the available well data, three lithology and its fluid content are classified as gas sand, brine sand, and shale. The likelihood function of these lithology is computed using Gaussian distribution and the prior probability is estimated by Markov-chain approach. Based on the prior information, the posterior probability is iteratively calculated by using values from elastic parameter section that is inverted from seismic data. The resulting probability section of each lithology will have value ranging from 0 to 1. The maximum-a-posteriori (MAP) in every location in the section is concluded as the most probable lithology to be discovered. The result shows that the distribution of gas sand can be predicted quite well by using acoustic impedance and Vp/Vs ratio. This is proven by a good fit between the predicted lithology section and the well.]]>
