Reservoir Forecast Optimism - Impact of Geostatistics, Reservoir Modeling, Heterogeneity, and Uncertainty

Summary The oil and gas industry uses static and dynamic reservoir models to assess volumetrics and to help evaluate development options. The models are routinely generated using sophisticated software. Very elegant geological models are often generated without a full understanding the limitations imposed by the available data or of the underlying stochastic algorithms. Key issues facing reservoir modelers that have been evaluated include use of reasonable semivariogram model parameters (e.g. range, form, and nugget), model grid size, and model complexity. Within the last decade there has been increased recognition that incorporating uncertainty into reservoir modeling yields better business decisions, generally decreases project cycle time, and enables better understanding of the impact of reducing specific uncertainties through additional data acquisition. The robust incorporation of a reasonable uncertainty description in static and dynamic models significantly improves business decisions. The use of stochastic earth models combined with well placement optimization workflows is likely to yield significantly optimistic forecasts. Well placement optimization should be based on property distributions derived via appropriate estimation methods rather than stochastic methods. The oil and gas industry is in general moving away from an “honor the data” paradigm to an “honor the data and respect/incorporate uncertainty” paradigm for reservoir modeling.