A new procedure to reduce uncertainties in reservoir models using statistical inference and observed data

Abstract

Abstract A new procedure to reduce uncertainties in reservoir simulation models using statistical inference and observed data is presented in this paper. Observed data is used to guide the obtainment of posterior probability density functions ( pdf ). The process starts by sampling from prior pdf . Through an iterative procedure, posterior pdf is obtained in each iteration, which is used to sample new models for the next iteration, until a stop criterion is reached. A detailed relationship between all uncertain attributes defined in the analysis and well responses (water and oil rate, flow pressure etc.) is performed. Finally, final ensembles are sampled in order to assess the dispersion of the responses before and after the reduction of uncertainty. In order to assess the robustness of the proposed method, three reservoir models were studied. The first case is a simple reservoir with eight uncertain attributes and was used for validation purposes. The second case, is a more complex synthetic model with 16 uncertain attributes. Finally, the application to a real field is shown. The results showed that the proposed method has potential to deal with the problem of reservoir uncertainty management.