A computer-based method of conditioning a reservoir model to data, the method comprising: performing a modeling process within a stratigraphic grid corresponding to a subsurface volume to generate an initial model being a reservoir model having a corresponding model volume and including one or more facies objects within the grid, the modeling process including parametric distributions, initial and boundary conditions as well as depositional and erosional rules to define interactions among facies objects within the grid, the modeling process conditioning to an extent the initial model to known data and potential input trend models, the known data comprising measured well data; measuring at one or more locations within the model volume a mismatch between the initial model and the known data and potential input trend models used during the modeling process, and deriving a locally variable constraint model from the local mismatch measurement; and using the locally variable constraint model to execute a multiple point statistics simulation within a smoothing window surrounding a well rather than across the entire model volume, the locally variable constraint model constraining the multiple point statistics simulation by enforcing a gradation of level of constraint within the smoothing window such that it varies between completely constrained by the initial model at locations where the initial model is consistent with the known data and potential input trend models, and completely unconstrained by the initial model at locations where the initial model does not match the known data or potential input trend models, thereby correcting the initial model to improve conformance to the known data and potential input trend models. performing a modeling process within a stratigraphic grid corresponding to a subsurface volume to generate an initial model being a reservoir model having a corresponding model volume and including one or more facies objects within the grid, the modeling process including parametric distributions, initial and boundary conditions as well as depositional and erosional rules to define interactions among facies objects within the grid, the modeling process conditioning to an extent the initial model to known data and potential input trend models, the known data comprising measured well data; measuring at one or more locations within the model volume a mismatch between the initial model and the known data and potential input trend models used during the modeling process, and deriving a locally variable constraint model from the local mismatch measurement; and using the locally variable constraint model to execute a multiple point statistics simulation within a smoothing window surrounding a well rather than across the entire model volume, the locally variable constraint model constraining the multiple point statistics simulation by enforcing a gradation of level of constraint within the smoothing window such that it varies between completely constrained by the initial model at locations where the initial model is consistent with the known data and potential input trend models, and completely unconstrained by the initial model at locations where the initial model does not match the known data or potential input trend models, thereby correcting the initial model to improve conformance to the known data and potential input trend models. <EOS>