Our calibrated model has poor predictive value: An example from the petroleum industry

It is often assumed that once a model has been calibrated to measurements then it will have some level of predictive capability, although this may be limited. If the model does not have predictive capability then the assumption is that the model needs to be improved in some way.Using an example from the petroleum industry, we show that cases can exit where calibrated models have limited predictive capability. This occurs even when there is no modelling error present. It is also shown that the introduction of a small modelling error can make it impossible to obtain any models with useful predictive capability.We have been unable to find ways of identifying which calibrated models will have some predictive capacity and those which will not.