This paper proposes an approach for assessing a reservoir-simulation model for use in estimating reserves. A simulation model can integrate complex static data, the physical description of displacement processes, production constraints, and schedules. Hence, it can provide important information for business decisions and reserves estimation. Confidence in simulation predictions depends on the strength of evidence for the input data, quality control of the model, robustness of the history match, and whether there is independent evidence supporting predictions. We explain the principles for evaluating a simulation model and propose requirements for simulation predictions to be considered as proved reserves. This involves evaluation against different strands of evidence, such as static and dynamic characterization, wells and facilities description, reservoir performance, and analogs. Simulation models are often built to support business decisions by use of the best technical estimates for inputs. There can be instances where a simulation model may be reasonable and reliable but it only represents a “best technical” outcome. There may not be sufficient evidence to count the whole predicted recovery as proved reserves. We propose how such a model may be modified to also provide proved-reserves estimates. The approach can be used with different available data and at different stages of field life. It is illustrated through a case study that shows how the principles may be applied.