Historically, and across the E&P industry, several projects were found to underperform in their actual performance as compared to the promises made at the time of project approval. Investment decisions on upstream projects rely, to a large extent, on the robustness of the predicted ultimate recovery and production forecast associated with the chosen development concept. A 2010 upstream industry benchmarking consortium reviewed global performance of E&P projects and concluded that the majority of production forecasts presented at project approval were not attained. In many cases, this underdelivery resulted in eroded project economics and reputation damage with stakeholders. The challenge for the E&P industry is to ensure that project approvals are based on realistic forecasts (P50 case). Modern technology has offered a paradigm shift in the amount of detail which can be included in the subsurface static and dynamic models. However, a universal truth is that no single model is the perfect representation of reality, and the recovery processes based on modelling are, therefore, always approximations of real life situations. Simulation models are guiding tools rather than sources of perfect answers. This work is intended to increase awareness amongst the forecasters and the decision makers about pitfalls associated with production forecasting (especially those generated by dynamic models). It provides insight into the need to condition model input data or alternatively the model output to ensure that forecasts and resource volumes generated by these models are realistic. Root causes of unrealistic forecasts are identified. Dos and don'ts are described that help instil realism in the forecasting process. Pragmatic techniques to adjust the output obtained from dynamic modelling are illustrated using real-life examples. This work presents a pragmatic approach to create reliable long-term production forecasts which can form the basis for sound business decisions.
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