oa Reservoir Performance Forecasting – How Well Are We Really Doing?

Reservoir forecasts tend to be optimistic. Forecasts for IOR/EOR projects tend to be particularly optimistic. Sources of the optimism can be divided into several broad categories including: Data – quantity, quality, sampling bias; Static Modeling – model complexity, particularly for permeability contrasts; Model parameter/algorithms choice; and, Dynamic Modeling – model detail/complexity, up-scaling, well location optimization. In addition, human factors also tend to drive projects towards optimistic forecasts. Based studies of a number of reservoirs representing a variety of lithology types and depositional environments with data densities ranging from low (greenfield) to extremely high (multi-pattern pilots) observations on modeling and forecast accuracy can be made relative to IOR/EOR forecast results, in particular. Among the most critical modeling parameters are the areal grid size and the semivariogram range parameter. Optimistic estimates of the in place hydrocarbon volume is also one of the most significant sources of optimistic forecasts. Some of this latter bias is due to sampling, particularly for green-field developments, and some due to inappropriate use of analogs. This bias can be reduced with uncertainty-based analyses and workflows and an appropriate suite of analogs. Well location optimization based on stochastic models is an under-appreciated source of forecast optimism.