Uncertainty quantification and reduction through Data Assimilation approaches for the geomechanical modeling of hydrocarbon reservoirs

The use of numerical models in geomechanics implicitly assumes a number of approximations and uncertainties, even though they are usually regarded as deterministic tools. Simplifications in the constitutive law, uncertainties in geomechanical parameters values, imposition of boundary conditions are only few examples of the probabilistic factors that affect the modelling process of natural phenomena. Integration of Data Assimilation (DA) techniques in the modelling processing chain can improve the outcome accuracy and reliability by incorporating the available observation data. In this work, three different DA techniques have been integrated into a geomechanical reservoir model with the aim at improving land subsidence prediction over producing hydrocarbon fields. A synthetic test case has been analyzed demonstrating that the proposed approach could be a promising tool to improve the effectiveness and reliability of geomechanical reservoir models.