The oil and gas industry uses static and dynamic reservoir models to generate production forecasts. Generally, industry look-backs (and informal networking) have shown that forecasts are often optimistic for both "Greenfield" projects with limited data and "Brownfield" projects with abundant data. One of the main sources of optimistic forecasts is biased estimates of the original or net/targeted in place hydrocarbon volume. Some bias is due to sampling, particularly for Greenfield developments, and this bias can be reduced statistically or by use of appropriate uncertainty-based workflows together with a reasonable uncertainty assessment that includes the available data and an appropriate suite of analogs. An underappreciated additional source of significant bias related to in place volumes is the use of a stochastic reservoir property model to locate wells. The use of stochastic earth models combined with well placement optimization workflows is likely to yield significantly optimistic forecasts. Well placement and optimization should be based on property distributions derived via estimation methods such as kriging rather than stochastic methods.Reservoir models are usually generated using sophisticated software. Elegant geological models can be generated without an adequate understanding of the limitations imposed by the available data, associated uncertainty, or the underlying stochastic algorithms and their input requirements (e.g. the semivariogram; a measure of heterogeneity). For example, forecasts based on models generated using different semivariogram ranges (all other input parameters held constant) show that the recovery factor for waterflooding may be impacted significantly whereas for steamflooding the impact may be negligible. Recent studies using an extensively sampled portion of a heavy oil carbonate reservoir have shown that grid size, which has minimal effect on primary recovery forecasts, will impact forecasts for displacement processes. Generally, if there are fewer than ten cells between injectors and producers in dynamic models, waterflood forecasts and perhaps also steamflood forecasts will be optimistic. The impact of static and dynamic model parameter choices on forecast bias should be evaluated as part of any comprehensive reservoir study.