When large volumes of fluids are removed from or injected into underground formations for, e.g., hydrocarbon and water production, CO_2 storage, gas storage, and geothermal energy exploitation, monitoring of surface deformations coupled to numerical modeling improves our understanding of reservoir behavior. The ability to accurately simulate surface displacements, however, is often impaired by limited information on reservoir geometry, waterdrive strength, and fluid-geomechanical parameters characterizing the geologic formations of interest. We have investigated the ability of efficient global optimization (EGO) to reduce the parameter uncertainties usually affecting geomechanical modeling. EGO is used to identify the parameter set that minimizes the difference in land displacements obtained from synthetic aperture radar (SAR)-derived measurements and 3D geomechanical modeling. We have tested the approach on the Tengiz giant oil field, Kazakhstan, where large uncertainties affect our knowledge of geomechanical parameters and pore pressure evolution. SqueeSAR on ENVISAT and RADARSAT-1 images acquired between 2004 and 2007 provided a set of high-precision, high-areal-density subsidence measurements of the test site. Based on the available information, a 3D geomechanical model of the reservoir has been developed using the elastoplastic finite-element code GEPS3D. Our results indicated that EGO efficiently identifies the global optimum in the parameter space, yielding a significant reduction in the difference between modeled and measured land subsidence. The match between simulated and SAR-measured horizontal displacements was developed as validation of the EGO calibration, which thus proved an effective and rather inexpensive method for the simultaneous management of several uncertainties and the reliable quantification of the rock properties.