Methodology for data assimilation in reservoir and production system to improve short- and medium-term forecast

Numerical reservoir simulation is an important tool that widely used in the petroleum industry for production forecast and decision-making processes. In a context of integrated system involving reservoir, wells, facilities, quality depends not only on models well productivity/injectivity but also boundary condition future extrapolation which, turn, characteristics. Both systems, are characterized by uncertain properties behavior, such as porosity permeability, among others, reservoir; pipeline roughness, multiphase flow behavior pipes, system. This paper proposes methodology dynamic data assimilation to reduce uncertainty both improve short- medium-term forecast. The was applied benchmark case built using real from offshore field Campos Basin (Brazil). results this study show it perform process systems obtain more reliable forecasts. We that, focusing forecast, uncertainties cause variability same order or magnitude. Based recommended consider multiple solutions realistic